4-ASKA Patents 2of3 20241031

Written by: Paul Lowndes <[email protected]>

Table of Contents

Patent Group V. Decentralized Governance and Auditing

Patent 13: Secure and Transparent Zonal Governance System with AI-Driven Authentication, Decentralized Ledger, and Secure Boot Integration

Patent 14: 3D-Printed Microstructure Audit Trail for Citizen Voting System

Patent 15: AI-Powered Governance Auditing and Transparency with TRC Monitoring and Automated Conflict Resolution

Patent 16: Automated Evolutionary Software Development with Secure, Zoned Deployment, TRC-Based Verification, and Adaptive AI-Driven Security

Patent 17: Multi-Dimensional Audit Trail System for ASKA with AI-Driven Microstructure Analysis and Software Provenance Tracking

Patent Group VI. Secure Collaboration and Data Management

Patent 18: Secure and Adaptive Hyper-Virtualization System for Collaborative Workloads with Decentralized Policy Management, Real-time Security Monitoring, and Privacy-Preserving Data Sharing

Patent 19: Privacy-Preserving Federated Learning System using Secure Multi-Party Computation across Isolated Execution Stacks

Patent 20: Secure Data Enclave System with Privacy-Preserving Collaborative Data Analytics

Patent 21: Blockchain-Enabled Self-Evolving Software System with 3D-Printed Microstructure Provenance Tracking and AI-Driven Security

Patent 22: Secure Inter-Zone Collaboration Framework with Privacy-Preserving Data Exchange and Distributed Ledger Synchronization

Patent Group VII. Miscellaneous

Patent 23: Adaptive Context-Aware MFA with Biometric and Behavioral Analysis

Patent 24: Hardware-Enforced Secure Encrypted Enclave for Data at Rest (HESE-DAR) with Dynamic Resource Allocation and Decentralized Governance Integration.

Patent 25: Dynamically Reconfigurable Capability-Based Inter-IES Communication for ASKA

Patent 26: Capability-Enhanced Packet-Carried Forwarding State for Secure Inter-Component Communication in Multi-Kernel Systems

Patent 27: Sovereign Trust Network for Secure Key Management and Authentication with Multi-Level Control and Recovery System

Patent 28: System and Method for Adaptive Secure Inter-Zone Communication Across Authenticated and Sovereign Trust Networks

Patent 29: Quantum-Entangled One-Time Pad Module for Secure Computing Architectures

BRAINSTORM PATENT 29:

Patent Group V. Decentralized Governance and Auditing

Patent 13: Secure and Transparent Zonal Governance System with AI-Driven Authentication, Decentralized Ledger, and Secure Boot Integration

Abstract:

This invention presents a secure and transparent zonal governance system for a decentralized computing environment, leveraging AI-driven voter authentication, a distributed ledger, and secure boot integration. The system employs a multi-faceted, context-aware approach to voter authentication, dynamically adjusting security thresholds based on real-time risk assessments, contextual factors (user location, device posture, threat intelligence), voter reputation, voting history, and zone-specific trust policies.  Privacy-preserving techniques, including homomorphic encryption and zero-knowledge proofs, protect sensitive biometric data and ensure the confidentiality of votes.  Validated votes, voter registration information, eligibility criteria, and voting policies are securely recorded on a quantum-resistant decentralized ledger, using a distributed consensus protocol for integrity and immutability.  Furthermore, the system integrates with ASKA's Secure UI Kernel (Patent 11) for secure user interaction and a 3D-printed microstructure audit trail system (Patent 14) for enhanced auditability and tamper evidence.  A novel bootstrapping certificate mechanism ensures the authenticity and integrity of voting terminals and integrates seamlessly with the attestation process. This comprehensive solution provides a robust, adaptable, and privacy-preserving framework for trustworthy governance in a decentralized, zoned environment.

Diagram:

graph TD

    Voter["Voter"] --> IAM["Identity & Access<br> Management (IAM)"]

    IAM --> Authentication["Authentication<br>(Hardware Token/Biometrics)"]

    Authentication -- Validated Identity --> AI_Engine["AI-Powered<br>Authentication Engine"]

    Authentication -- Invalid Identity --> Access_Denied["Access Denied"]

    subgraph Biometric_Verification["Biometric Verification"]

        Biometrics["Biometric Capture<br>(Secure UI - Patent 11)"]

        Biometrics --> Bio_Data["Biometric Data"]

        Bio_Data --> MPC_Engine["MPC Engine (Privacy-Preserving)"] 

        MPC_Engine --> Bio_Score["Biometric Score"]

        Bio_Score --> AI_Engine

    end

    Authentication --> Biometric_Verification

    subgraph Trust_Context["Trust Context"]

        Reputation["Voter<br>Reputation"]

        History["Voting<br>History"]

        Threat_Intel["Threat<br>Intelligence"]

        Reputation --> AI_Engine

        History --> AI_Engine

        Threat_Intel --> AI_Engine

    end

    AI_Engine -- Authenticated --> Voting_Booth["Secure Voting Booth"]

    Voting_Booth --> Vote["Cast Vote"]

    Vote --> Encryption["Encryption (Patent 5/24)"]

    Encryption --> Secure_Channel["Secure Channel (Patent 3)"]

    subgraph DLT["Decentralized Ledger (DLT)"]

        Secure_Channel --> DLT_Node_1["DLT Node 1"]

        Secure_Channel --> DLT_Node_N["... DLT Node N"]

        DLT_Node_1 --> Ledger["Immutable Vote Record"]

        DLT_Node_N --> Ledger

    end

    style AI_Engine fill:#ccf,stroke:#888,stroke-width:2px

    style DLT fill:#aaf,stroke:#444

    style Biometric_Verification fill:#f9f,stroke:#333

Description of Diagram:

This diagram details the AI-driven voter authentication and decentralized ledger system described in Patent 13.

  1. Voter and Initial Checks:
  1. AI-Powered Authentication Engine: This is the core component that makes the final authentication decision, taking into account various factors:
  1. Secure Voting and Recording:
  1. Decentralized Ledger (DLT):

Key Features and Interactions:

This diagram comprehensively visualizes Patent 13, clarifying the authentication process, the role of AI and biometrics, and how votes are securely recorded on the decentralized ledger.  It demonstrates the patent's contribution to ASKA's robust and transparent governance system.

Claims:

  1. (Independent) A secure governance system for a computing system comprising a plurality of Modular Isolated Execution Stacks (IES) (Patent 1) organized into a hierarchy of Zones (Patent 18), each Zone associated with a Trust Root Configuration (TRC) stored on a decentralized, tamper-proof ledger (Patent 15), comprising:

a. a plurality of physically isolated Voting Terminals, each terminal associated with a specific Zone, having a unique cryptographic identifier, and further comprising a secure boot mechanism (Patent 1) that verifies the terminal's integrity using a bootstrapping certificate issued by a trusted authority within said Zone, and an attestation process (Patent 13) that generates a signed attestation report recording the terminal's security posture; b. an AI-powered Authentication Engine within each Voting Terminal that: i. analyzes authentication data, dynamically adjusting security thresholds based on real-time risk assessments, contextual factors (user location, device posture, threat intelligence), voter reputation, voting history, and trust policies defined in the TRC of the associated Zone; and ii. verifies presented voter credentials, using at least one of: hardware-based identity tokens, biometric verification, a unique, tamper-evident 3D-printed microstructure (Patent 14), or a combination thereof, wherein authentication methods and security thresholds are dynamically adjusted based on real-time risk assessments and trust policies defined in the relevant TRC; and c. a Decentralized Ledger (Patent 15) storing voter registration information, eligibility criteria, zone-specific voting policies, and validated votes recorded using privacy-preserving techniques, wherein said ledger uses a distributed consensus protocol and quantum-resistant cryptography (Patent 5) to ensure the integrity and immutability of records.

  1. (Dependent) The system of claim 1, wherein said AI-powered Authentication Engine utilizes machine learning algorithms to analyze biometric data, detect anomalies and potential fraud attempts, and dynamically adjust security thresholds and authentication methods during the authentication process.

  1. (Dependent) The system of claim 1, wherein voter registration information, eligibility criteria, and zone-specific voting policies stored on said Decentralized Ledger are cryptographically protected using a combination of digital signatures (Patent 24), secure multi-party computation (Patent 19), and zero-knowledge proofs (Patent 6).

  1. (Dependent) The system of claim 1, wherein validated votes are recorded on said Decentralized Ledger using homomorphic encryption (Patent 20) or other privacy-preserving techniques to ensure the confidentiality of individual votes while maintaining the integrity and verifiability of election results, and wherein the vote records are linked to corresponding voter identifiers and authentication audit trails (Patent 17) recorded during the authentication process.

  1. (Dependent) The system of claim 1, wherein said Voting Terminals are integrated with a Secure UI Kernel (Patent 11) to:    a) ensure that user interactions during the authentication process occur within a secure and isolated environment; and    b) dynamically adjust the trust levels of different UI regions based on the context and origin of displayed information, protecting against UI-based attacks and preventing information leakage.

  1. (Dependent) The system of claim 1, further comprising a 3D-printed microstructure audit trail system (Patent 14) that generates a unique, tamper-evident 3D microstructure for each voter or voting session, for recording validated votes and authentication events, and for use as a physical second factor for authentication, wherein each microstructure is cryptographically linked to corresponding records on the decentralized ledger.

  1. (Dependent) The system of claim 1, wherein said bootstrapping certificate for each Voting Terminal:    a) is issued by a designated Certificate Authority within the associated Zone, with certificate issuance and revocation managed by a decentralized governance system (Patent 13);    b) includes at least one of: the Voting Terminal's unique cryptographic identifier, a hash of its secure boot code, or other verifiable identification information; and     c) is verified by the AI-powered Authentication Engine during the boot process and before initiating any voter authentication procedures.

  1. (Dependent) The system of claim 1, wherein said attestation process integrates with said secure boot mechanism and generates a signed attestation report including the status of said bootstrapping certificate and other security-relevant information about the Voting Terminal.

Patent 14: 3D-Printed Microstructure Audit Trail for Citizen Voting System

Abstract:

This invention introduces a novel system for generating a secure and verifiable physical audit trail for citizen voting in elections.  The system utilizes advanced 3D-printing technology to create unique, tamper-evident microstructures that serve as physical representations of individual votes. Each microstructure incorporates a unique identifier encoded in its physical geometry, providing a robust and compact audit trail that can be verified independently of electronic voting records.

These microstructures are generated during the voting process and stored securely within a tamper-evident container, forming a physical chain of custody.  The system seamlessly integrates with the ASKA governance and authentication system, ensuring secure voter identification and vote recording. A dedicated verification module enables authorized auditors to scan and decode the microstructures, providing a transparent and auditable record of each vote cast, bolstering public trust and confidence in the electoral process.

Diagram:

graph TD

    subgraph Voting_Terminal["Secure&nbsp;Voting&nbsp;Terminal&nbsp;(Patent&nbsp;13)"]

        Voter["Voter (Authenticated)"] --> Ballot["Cast Ballot"]

        Ballot --> Vote_Recorder["Vote Recorder"]

        Vote_Recorder --> Data["Vote Data (Encrypted)"]

        Data --> Secure_Channel["Secure Channel (Patent 3)"]

        Secure_Channel --> DLT["Decentralized Ledger<br>(Patents 13,15)"]

        Vote_Recorder --> Microstructure_Gen["Microstructure Generator"]

        Microstructure_Gen --> Storage["Tamper-Evident Storage"]

    end

    subgraph Verification_Station["Microstructure&nbsp;Verification&nbsp;Station"]

        Storage_Retrieval["Retrieve Microstructures<br>from Secure Storage"]

        Storage --> Storage_Retrieval

        Storage_Retrieval --> Scanner["Microstructure Scanner"]

        Scanner --> Decoder["Microstructure Decoder"]

        Decoder --> Verified_Votes["Verified Votes"]

    end

   

    subgraph Audit_Records["Audit Records"]

        DLT --> Audit_Log["Digital Audit Log"]

        Verified_Votes --> Physical_Log["Physical Audit Log"]

        AI["AI-Driven Comparator <br> (Patent 15, 17)"] --> Discrepancies["Discrepancies/Alerts"]

        Audit_Log --> AI

        Physical_Log --> AI

    end

Description of Diagram:

This diagram illustrates the process of generating and verifying 3D-printed microstructures for a secure audit trail in a citizen voting system, as described in Patent 14.

  1. Secure Voting Terminal (Patent 13): This subgraph represents the secure voting terminal where votes are cast and microstructures are generated.

  1. Microstructure Verification Station: This subgraph represents the station where microstructures are retrieved from storage, scanned, and decoded for verification and comparison.

  1. Audit Records: This subgraph represents the process of comparing digital and physical audit logs.

Key Features and Interactions:

This diagram visually explains the key innovations of Patent 14 and their role in creating a secure and verifiable audit trail for citizen voting. It clarifies the process of microstructure generation and verification, emphasizing the system's transparency and resistance to manipulation.

Claims:

  1. 3D-Printed Microstructure as a Physical Vote Representation:

  1. Unique Microstructure Identifier Encoding:

  1. Tamper-Evident Microstructure Design:

  1. Secure Microstructure Storage and Chain of Custody:

  1. Integration with ASKA Governance and Authentication:

  1. Microstructure Verification Module for Audit Trail Validation:

  1. Compact and Scalable Microstructure Design:

Novel Claims Drawing on External Research:

  1. Multi-Material 3D Printing for Enhanced Security:

  1. Microfluidic Channels for Tamper-Evident Ink Encapsulation:

  1. DNA-Based Microstructure Tagging for Ultimate Security:

Patent 15: AI-Powered Governance Auditing and Transparency with TRC Monitoring and Automated Conflict Resolution

Abstract:

This invention discloses an AI-powered governance auditing and transparency system for ASKA, enhancing accountability and trust within a decentralized, zoned environment. The system features an AI-driven Auditing Module that analyzes voting records, policy decisions, system events, and Trust Root Configurations (TRCs) for anomalies and inconsistencies, correlating digital records with physical microstructures and software provenance. An Automated Conflict Resolution mechanism addresses inconsistencies between TRCs, promoting system stability. A real-time policy simulation engine allows for proactive governance and citizen feedback. Integration with a decentralized, tamper-proof ledger ensures transparency and auditability, while access control mechanisms protect sensitive information. This comprehensive approach provides robust oversight and promotes trustworthy governance within ASKA.

Diagram:

graph TD

    subgraph Governance_System["Governance&nbsp;System&nbsp;(Patents&nbsp;13,&nbsp;14)"]

        Voting["Voting Terminals (Patent 13)"] --> DLT["Decentralized Ledger<br>(Patents 13,15)"]

        DLT --> Voting_Records["Voting Records"]

        Voting --> Microstructure_Gen["Microstructure Generator (Patent 14)"]

        Microstructure_Gen --> Microstructures["3D Microstructures"]

    end

    subgraph Auditing_System["AI&nbsp;Powered&nbsp;Auditing&nbsp;System&nbsp;(Patent&nbsp;15)"]

        Voting_Records --> AI_Auditor["AI-Driven Auditor"]

        Microstructures --> AI_Auditor

        Microstructure_Verifier["Microstructure Verifier (Patent 14,17)"] --> AI_Auditor

        AI_Auditor --> Audit_Reports["Audit Reports"]

        AI_Auditor --> Anomaly_Alerts["Anomaly Alerts"]

    end

    subgraph Policy_Simulation["Real-Time Policy Simulation"]

        Proposed_Policies["Proposed Policies"] --> Simulator["Policy Simulator"]

        Simulator --> Predicted_Impact["Predicted Impact"]

        Citizen_Feedback["Citizen Feedback"] --> Simulator

    end

    Auditing_System ----> Policy_Simulation

    DLT --> Policy_Simulation

    Auditing_System --> Secure_UI["Secure UI (Patent 11)"] -->|Audit Reports/Alerts| Secure_UI

    Policy_Simulation --> Secure_UI -->|Predicted Impact| Secure_UI

    style AI_Auditor fill:#ccf,stroke:#888,stroke-width:2px

    style Simulator fill:#aaf,stroke:#444

Description of Diagram:

This diagram illustrates the components and interactions of the AI-powered governance auditing and transparency system described in Patent 15.

  1. Governance System (Patents 13, 14):  This subgraph represents the core governance components.
  1. AI-Powered Auditing System (Patent 15):  This subgraph represents the core auditing components.
  1. Real-Time Policy Simulation: This subgraph represents the policy simulation engine.
  1. Secure UI (Patent 11):  Displays audit reports, anomaly alerts, and predicted policy impacts to authorized users and citizens.

Key Features and Interactions:

Claims:

  1. A secure governance auditing system for a computing system comprising a plurality of Modular Isolated Execution Stacks (IES) (Patent 1) organized into a hierarchy of Zones (Patent 18), each Zone associated with a Trust Root Configuration (TRC) stored on a decentralized, tamper-proof ledger (Patent 15), comprising:

a) a plurality of physically isolated execution environments for processing and auditing governance data; b) an AI-powered Auditing Module that analyzes voting records (Patent 13), policy decisions, system events, and TRCs for anomalies, inconsistencies, or potential security breaches, comparing digital records on the ledger with corresponding physical microstructures (Patent 14) and software provenance records (Patent 17); and c) an Automated Conflict Resolution mechanism that attempts to resolve detected inconsistencies between TRCs of different Zones based on predefined rules or a distributed consensus protocol, and records the resolution outcomes on the decentralized ledger.

  1. The system of claim 1, wherein said AI-powered Auditing Module:

a) monitors TRCs for changes; b) verifies the authenticity and integrity of TRC updates using digital signatures and cross-signatures between TRCs of connected Zones (Patent 4); and c) records all TRC changes and verification results on the decentralized ledger.

  1. The system of claim 1, wherein said AI-powered Auditing Module detects and reports inconsistencies between TRCs of different Zones, including conflicts in at least one of:

a) trust root configurations; b) security policies; or c) resource allocation rules.

  1. The system of claim 3, wherein said Automated Conflict Resolution mechanism:

a) utilizes a distributed consensus protocol involving representatives from affected Zones to negotiate a compromise solution; and b) records the outcome of the negotiation on the decentralized ledger.

  1. The system of claim 1, wherein said AI-powered Auditing Module correlates digital records on the ledger with corresponding physical microstructures generated by a 3D-printed microstructure audit trail system (Patent 14), detecting anomalies that could indicate at least one of:

a) tampering; or b) inconsistencies.

  1. The system of claim 1, further comprising a Real-Time Policy Simulation Engine that:

a) allows users to interact with proposed policy changes and explore their potential impact on system behavior and security; b) allows users to provide feedback on proposed policy changes; and c) records the simulation results and feedback on the decentralized ledger.

  1. The system of claim 1, wherein:

a) the decentralized ledger is accessible to authorized individuals and the public, providing transparent and auditable access to governance data, TRCs, and conflict resolution outcomes; and b) access control mechanisms are employed to protect sensitive information.

  1. The system of claim 1, wherein said AI-powered Auditing Module:

a) employs machine learning algorithms to detect anomalies and potential vulnerabilities in real-time; and b) generates alerts and notifications to designated authorities.

  1. The system of claim 1, further comprising a Predictive Analysis Module that utilizes AI algorithms to identify potential governance issues or vulnerabilities before they manifest, based on at least one of:

a) historical data; b) trend analysis; or c) trust levels derived from the DTMS (Patent 4).

Patent 16: Automated Evolutionary Software Development with Secure, Zoned Deployment, TRC-Based Verification, and Adaptive AI-Driven Security

Abstract:

This invention presents an Automated Evolutionary Software Development System (AESDS) for the ASKA ecosystem, enabling secure, adaptable, and autonomous software evolution within a zoned, multi-kernel environment.  The AESDS integrates with Modular Isolated Execution Stacks (IES) and leverages AI-driven algorithms to generate, refine, and deploy software updates, adapting to evolving threats and user needs.  Informed by a knowledge base, performance metrics, user feedback, and threat intelligence, the AI engine produces software candidates that undergo rigorous testing for compatibility, security, and performance in isolated sandboxed environments within IES instances. A decentralized governance framework, incorporating blockchain technology and AI-driven policy analysis, governs software approval and deployment, ensuring transparency and accountability. The AESDS distributes software updates securely via authenticated SCION paths, preventing interception or tampering.  TRCs verify the authenticity and integrity of updates, ensuring that only authorized software is installed. An Adaptive AI-Driven Security module analyzes real-time system behavior, threat intelligence, and vulnerability reports delivered via SCION Control Message Protocol (SCMP), proactively generating security patches, policy adjustments, and resource allocation optimizations.  Furthermore, the AESDS proactively incorporates defenses against timing side-channel attacks by embedding mitigation techniques, such as constant-time algorithms and compiler-level obfuscation, directly into generated software artifacts. This comprehensive approach creates a self-evolving software ecosystem that continuously adapts to the dynamic threat landscape while maintaining robust security within ASKA.

Diagram:

graph TD

    subgraph "AESDS (Automated Evolutionary Software Development System)"

        KB["Knowledge Base<br>(Best Practices, Libraries)"] --> AI_Engine

        Metrics["Performance Metrics"] --> AI_Engine

        User_Feedback["User Feedback"] --> AI_Engine

        AI_Engine["AI Engine<br>(Code Gen & Refinement)"] --> Code_Gen

        Code_Gen["Code Generator"] --> Sandbox

        Threat_Intel["Threat Intelligence"] --> Adaptive_Security

        Sandbox["Sandbox Environment<br>(IES Instance)"] --> Multi_Layered_Validation

       

        subgraph "Multi-Layered Validation"

            Compatibility_Testing["Compatibility Testing"]

            Security_Testing["Security Testing"]

            Performance_Testing["Performance Testing"]

        end

        Multi_Layered_Validation --> Software_Artifact

        Software_Artifact["Software Artifact"] --> Policy_Analysis

        Adaptive_Security["Adaptive AI-Driven Security<br>(Patch Generation)"] --> Software_Artifact

        subgraph "Decentralized Governance Framework"

            Policy_Analysis["AI-Driven Policy Analysis"]

            Policy_Analysis -- Recommendation --> Consensus

            Consensus["Consensus Mechanism"] --> Blockchain

            Blockchain["Blockchain Ledger"] --> Secure_Repository

        end

        Secure_Repository["Secure Software Repository"] --> Secure_Distribution

        Secure_Distribution["Secure Distribution Network"] --> Deployment

        Deployment["Deployment to IES Instances"] --> Metrics

    end

Diagram Description:

This diagram provides a detailed view of the internal components and processes of the Automated Evolutionary Software Development System (AESDS).  It breaks down the AESDS functionalities and how they contribute to a secure and adaptive software development lifecycle.

Patent References:

This diagram provides a comprehensive visual representation of the AESDS, illustrating its key components and functionalities. It clarifies the complex process of automated software development within the secure ASKA environment and how it integrates with other ASKA technologies. It also reinforces the intellectual property protection of Patent 16 by detailing its inner workings.

Diagram 2 for Patent 16:

graph TD

    subgraph AESDS["AESDS&nbsp;(Automated&nbsp;Evolutionary&nbsp;Software&nbsp;Development&nbsp;System&nbsp;Patent&nbsp;16)"]

        AI_Engine["AI Engine (Code Gen & Refinement)"] --> Code_Generator["Code Generator"]

        KB["Knowledge Base"] --> AI_Engine

        Metrics["Performance Metrics"] --> AI_Engine

        User_Feedback["User Feedback"] --> AI_Engine

        Threat_Intel["Threat Intelligence"] --> AI_Engine

        Code_Generator --> Validator["Validator (Sandbox - Patent 1)"]

        Validator --> Software_Update["Software Update"]

    end

    Software_Update --> Signer["Digital Signer"]

    Signer --> Microstructure_Gen["Microstructure Generator (Patents 14, 17)"]

    Microstructure_Gen --> Microstructure["3D Microstructure"]

    Signer --> Blockchain["Blockchain Ledger (Patents 13, 15)"]

    Microstructure --> Blockchain

    Blockchain --> Deployer["Secure Deployer"]

    Deployer --> IES_Instances["IES Instances (Patent 1)"]

    IES_Instances --> Metrics

   

    style Microstructure fill:#ccf,stroke:#888,stroke-width:2px

    style Blockchain fill:#aaf,stroke:#444

Description for Diagram 2 for Patent 16:

This diagram illustrates the key components and processes of the Blockchain-Enabled Self-Evolving Software System (Patent 21), emphasizing the integration of blockchain, 3D microstructures, and the AESDS.

  1. AESDS (Patent 16): This subgraph represents the Automated Evolutionary Software Development System, responsible for generating software updates.  It includes the AI Engine, Knowledge Base, Performance Metrics input, User Feedback input, Threat Intelligence input, Code Generator, and Validator (which uses a sandboxed IES instance - Patent 1).

  1. Software Update:  Represents the new software generated by the AESDS.

  1. Digital Signer: Cryptographically signs the software update.

  1. Microstructure Generator (Patents 14, 17): Creates a unique 3D microstructure corresponding to the software update, embedding the digital signature within its physical geometry.

  1. 3D Microstructure: The physical, tamper-evident representation of the software update's integrity and provenance.

  1. Blockchain Ledger (Patents 13, 15):  Records the digital signature of the software update and the unique identifier of the associated microstructure.

  1. Secure Deployer:  Deploys the validated and authenticated software update to the IES Instances.

  1. IES Instances (Patent 1): The target execution environments for the software update.

  1. Metrics: Performance metrics collected from the updated IES instances are fed back into the AESDS to inform future software evolution.

Key Features Highlighted:

This diagram provides a clear and concise visualization of Patent 21, effectively communicating its key innovations and their integration within the ASKA ecosystem.  It clarifies the process of secure and verifiable software evolution, emphasizing the system's adaptability and resilience.

Diagram 3: Isomorphic Architecture Monitoring and Adaptation (IAMA)

graph TD

    subgraph "AESDS (Patent 16)"

        subgraph "Isomorphic&nbsp;Architecture&nbsp;Monitoring&nbsp;&&nbsp;Adaptation&nbsp;(IAMA)"

            Legacy_System["Legacy System"] --> Data_Diode["Data Diode (Patent 2)"]

            Data_Diode --> Monitor["Legacy System Monitor"]

            Monitor --> Isomorphic_Model["Isomorphic Model<br>(Legacy System Representation)"]

            Isomorphic_Model --> AI_Engine["AI Engine<br>(Anomaly Detection & Prediction)"]

            AI_Engine -- Predicted Vulnerabilities --> Patch_Generator["Patch Generator"]

        end

       

        KB["Knowledge Base"] --> AI_Engine

        Metrics["Performance Metrics"] --> AI_Engine

        User_Feedback["User Feedback"] --> AI_Engine

        AI_Engine --> Code_Gen["Code Generator"]

        Code_Gen --> Sandbox["Sandbox Environment (Patent 1)"]

        Threat_Intel["Threat Intelligence"] --> Adaptive_Security["Adaptive Security (Patent 7)"]

        subgraph "Multi-Layered Validation"

            Compatibility["Compatibility Testing"]

            Security["Security Testing"]

            Performance["Performance Testing"]

        end

        Sandbox --> Multi_Layered_Validation

        Patch_Generator --> Multi_Layered_Validation

       

        Multi_Layered_Validation --> Software_Artifact["Software Artifact"]

        Adaptive_Security --> Software_Artifact

       

        Software_Artifact --> Policy_Analysis["AI-Driven Policy Analysis (Patent 15)"]

        subgraph "Decentralized&nbsp;Governance&nbsp;(Patents&nbsp;13,&nbsp;15)"

            Policy_Analysis -- Recommendation --> Consensus["Consensus Mechanism"]

            Consensus --> Blockchain["Blockchain Ledger"]

        end

        Blockchain --> Secure_Repo["Secure Repository"]

        Secure_Repo --> Secure_Distribution["Secure Distribution (Patent 3)"]

        Secure_Distribution --> Deployment["Deployment<br>(to IES Instances)"]

        Deployment --> Metrics

        IAMA ----> AI_Engine

    end

    style IAMA fill:#ccf,stroke:#888,stroke-width:2px

Diagram 3 Description Isomorphic Architecture Monitoring and Adaptation (IAMA):

This detailed Mermaid diagram illustrates the integration of the Isomorphic Architecture Monitoring and Adaptation (IAMA) module within the Automated Evolutionary Software Development System (AESDS) of ASKA, corresponding to the new claims added to Patent 16.

AESDS (Patent 16):  This subgraph encompasses the entire AESDS, including the new IAMA module.

Isomorphic Architecture Monitoring & Adaptation (IAMA): This subgraph details the IAMA module and its interaction with the legacy system.

Core AESDS Components: These components represent the standard AESDS functionality.

Decentralized Governance (Patents 13, 15):  This subgraph represents the governance framework for software approval.

Deployment Process:

Diagram 4:

graph TD

    %% Secure Deployment & Verification subgraph

    subgraph "Secure&nbsp;Deployment&nbsp;&&nbsp;Verification"

        K --> N(Authenticated SCION Paths)

        N --> L

        K --> O(TRC Verification)

        O --> L

        style K fill:#bbf,stroke:#333,stroke-width:2px

        style N fill:#bbf,stroke:#333,stroke-width:2px

        style O fill:#bbf,stroke:#333,stroke-width:2px

    end

   

    %% AESDS subgraph

    subgraph "Automated&nbsp;Evolutionary&nbsp;Software&nbsp;Development&nbsp;(AESDS)"

        A[AI Engine] --> B(Knowledge<br>Base)

        A --> C(Performance<br>Metrics)

        A --> D(User<br>Feedback)

        A --> E(Threat Intelligence)

        A --> F{IAMA Module}

        A --> G[Code<br>Generator]

        G --> H(Sandbox<br>Environment)

        H --> I[Multi-Layered<br>Validation]

        I --> J(Software<br>Artifact)

        J --> K[Secure Zoned Deployment]

        K --> L(IES Instances)

        F --> M(Legacy System)

        M -.-> F

        style A fill:#ccf,stroke:#333,stroke-width:2px

        style J fill:#ccf,stroke:#333,stroke-width:2px

    end

    %% Adaptive Security subgraph

    subgraph "Adaptive Security"

        L --> P(System Behavior Monitoring)

        P --> A

        E --> A

        style P fill:#aaf,stroke:#333,stroke-width:2px

    end

    %% Link style

    linkStyle default stroke:#555,stroke-width:1px

Description for Diagram 4:

Legend:

This diagram clearly shows the AI-driven development process, the secure and verified deployment process using SCION and TRCs, and the feedback loop for adaptive security adjustments based on system behavior monitoring and threat intelligence.

Claims:

  1. A secure computing system comprising a plurality of Modular Isolated Execution Stacks (IES) (Patent 1) organized into a hierarchy of Zones (Patent 18), each Zone associated with a Trust Root Configuration (TRC) stored on a decentralized, tamper-proof ledger (Patent 15), and a secure communication mechanism (Patent 2) between said IES instances, further comprising an Automated Evolutionary Software Development System (AESDS) that:

a. autonomously generates, refines, and updates software artifacts for execution within the IES environment, utilizing AI-driven algorithms informed by a knowledge base of best practices, performance metrics feedback from deployed software, user feedback, and threat intelligence; b. incorporates a multi-layered validation process, including compatibility testing, security testing, and performance testing within isolated sandboxed environments (Patent 1) inside IES instances, generating validation reports stored on the decentralized ledger; c. distributes software updates to IES instances through authenticated SCION paths (Patent 3), preventing interception or tampering and ensuring updates originate from authorized sources within defined zones; and d. verifies the authenticity and integrity of software updates using the trust policies and trust roots defined in the relevant TRCs, ensuring only authorized and trusted software is installed.

  1. The system of claim 1, wherein said AESDS further comprises a decentralized governance framework that governs the approval and deployment of software updates, utilizing:

a. blockchain technology (Patent 15) to create a tamper-proof record of software changes, approvals, and deployments, wherein each software artifact is cryptographically signed and linked to a unique identifier on the blockchain; b. AI-driven policy analysis (Patent 15) to evaluate software updates against predefined security policies, ethical guidelines, and regulatory requirements, providing recommendations to authorized entities for approval or rejection of said updates; and c. a distributed consensus mechanism (Patent 13) to ensure updates are vetted and approved by multiple authorized entities before deployment, wherein approvals are recorded on the blockchain ledger.

  1. The system of claim 1, wherein said AESDS comprises an Adaptive AI-Driven Security module that:

a. analyzes real-time system behavior, threat intelligence data, and vulnerability reports received via SCION Control Message Protocol (SCMP) to identify, assess, and classify security threats according to severity and potential impact; and b. automatically generates responses to identified threats, including generating security patches and enhancements for deployed software components and system configurations,  proactively addressing vulnerabilities, and wherein said responses comprise at least one of: code modifications, policy updates, or resource allocation adjustments (Patent 10).

  1. The system of claim 1, wherein said AESDS generates software artifacts that incorporate defenses against timing side-channel attacks, embedding at least one of: branch balancing techniques, constant-time algorithms, or compiler-level obfuscation, into generated code to minimize timing variations and enhance resistance to timing-based attacks.

  1. The system of claim 1, wherein said AESDS utilizes a modular software architecture enabling the development and deployment of individual software components that can be easily integrated and updated within the IES environment, promoting scalability and flexibility of the software ecosystem.

  1. The system of claim 1, further comprising a secure software repository and distribution network for:

a. securely storing validated and approved software artifacts using encryption (Patent 5, Patent 24) and access controls based on TRC policies (Patent 4); and b. securely delivering software updates to IES instances using authenticated SCION paths (Patent 3) and a distributed consensus mechanism for secure and consistent update propagation and verification across zones.

  1. The system of claim 1, wherein said AESDS continuously monitors the performance and security of deployed software components within their respective IES instances, collecting data on resource utilization, execution time, and potential bottlenecks, and wherein said AESDS automatically triggers optimization or remediation procedures based on at least one of: detected performance bottlenecks, identified security vulnerabilities reported via SCMP, or policy updates from the decentralized governance framework, said procedures including at least one of: automated code refinement, resource reallocation (Patent 10), or dynamic scaling of IES instances (Patent 10), and recording all optimization and remediation actions on the decentralized ledger (Patent 15).

  1. The system of claim 1, wherein said AESDS integrates with ASKA's Multi-Dimensional Audit Trail System (MDATS - Patent 17) to:

a. record a comprehensive audit trail of all software development actions, from initial code generation to final deployment, including validation results, approvals, and deployment timestamps; and b. link each software update to a unique physical microstructure (Patent 14) and a digital signature recorded on the decentralized ledger (Patent 15), ensuring transparency and accountability.

  1. A secure computing system comprising a plurality of Modular Isolated Execution Stacks (IES) and an Automated Evolutionary Software Development System (AESDS) as described in Claim 1, further comprising:

an Isomorphic Architecture Monitoring and Adaptation (IAMA) module within said AESDS, wherein said IAMA module:

a. creates and maintains an isomorphic model of a connected legacy system;

b. monitors activity within said legacy system through secure, unidirectional communication channels;

c. uses said isomorphic model and AI-driven analysis to predict potential vulnerabilities or attack vectors within said legacy system; and

d. proactively generates and deploys security patches and updates for the ASKA system to mitigate predicted vulnerabilities, wherein said patches and updates are distributed and verified using the secure, zoned deployment mechanism of the AESDS.

  1. The system of claim 9, wherein said isomorphic model reflects the structure, communication patterns, and functionalities of the legacy system without containing sensitive data from said legacy system.

  1. The system of claim 9, wherein said unidirectional communication channels are implemented using data diodes.

  1. The system of claim 9, wherein said AI-driven analysis employs machine learning algorithms to identify anomalies and predict vulnerabilities.

  1. The system of claim 9, wherein said security patches and updates are deployed to IES instances through authenticated SCION paths and verified using Trust Root Configurations (TRCs).

  1. The system of claim 9, wherein said IAMA module continuously monitors the effectiveness of deployed patches and provides feedback to the AI engine to refine predictive models.

  1. The system of claim 9, wherein said IAMA module dynamically updates the isomorphic model to reflect changes in the legacy system’s architecture or software.

  1. The system of claim 9, wherein said IAMA module prioritizes predicted vulnerabilities based on their potential impact and likelihood of exploitation.

Description for Claim 9:

Isomorphic Architecture Monitoring and Adaptation Innovation:

This innovation introduces a proactive security mechanism within the Automated Evolutionary Software Development System (AESDS) called Isomorphic Architecture Monitoring and Adaptation (IAMA). IAMA leverages an isomorphic model of the legacy system within ASKA.  This isomorphic model mirrors the structure and functionality of the legacy system, allowing the AI engine to analyze its behavior and predict potential vulnerabilities or attacks before they impact ASKA.

Here's how it works:

  1. Isomorphic Model Creation: The AESDS creates and maintains an isomorphic representation of the legacy system's architecture and software.  This model doesn't contain actual sensitive data from the legacy system, but it accurately reflects its structure, communication patterns, and functionalities.

  1. Continuous Monitoring: The AI engine continuously monitors the activity within the legacy system through secure, unidirectional communication channels (data diodes).  It analyzes system calls, network traffic, and other observable behaviors.  This monitoring focuses on security-relevant events and patterns, not on sensitive data itself.

  1. Anomaly Detection and Prediction:  Using machine learning and the isomorphic model, the AI engine identifies anomalies and predicts potential vulnerabilities or attack vectors that could be exploited in the legacy system. The isomorphic model helps to contextualize observed behaviors and identify patterns indicative of malicious activity.

  1. Proactive Patch Generation: Based on the predicted vulnerabilities, the AI engine proactively generates patches and updates for the ASKA system.  These updates address potential weaknesses before they can be exploited, effectively immunizing ASKA against attacks that might target the legacy system's vulnerabilities.

  1. Automated Deployment: The AESDS automatically deploys the generated patches through its secure, zoned deployment mechanism (as described in Patent 16).  This ensures that ASKA remains up-to-date and protected against evolving threats originating from the legacy environment.

  1. Feedback Loop:  The performance and effectiveness of the deployed patches are monitored, providing feedback to the AI engine to refine its predictive models and improve the accuracy of future patch generation.

How it Fits into ASKA:

IAMA enhances ASKA's proactive security capabilities. It complements the existing security features (IES, DTMS, etc.) by providing a forward-looking defense against attacks that might exploit vulnerabilities in legacy systems connected to ASKA. This strengthens ASKA’s position as a robust and adaptive security solution, particularly in environments where legacy system integration is necessary.

Patent 17: Multi-Dimensional Audit Trail System for ASKA with AI-Driven Microstructure Analysis and Software Provenance Tracking

Abstract:

This invention presents a groundbreaking Multi-Dimensional Audit Trail System (MDATS) for the ASKA secure computing ecosystem, enhancing governance auditing and ensuring the integrity of software development processes.  The MDATS seamlessly integrates a 3D-printed microstructure audit trail (Patent 13) with AI-driven analysis and blockchain-based provenance tracking.

An AI-powered Auditing Module continuously monitors governance activities, correlating digital records on the decentralized ledger with their corresponding physical microstructures.  The AI module analyzes the physical characteristics of the microstructures for anomalies indicative of tampering or inconsistencies, providing an unparalleled level of auditability and security.

Furthermore, the MDATS extends the physical audit trail to encompass software development, securely linking each software update generated by the Automated Evolutionary Software Development System (AESDS) (Patent 15) to a unique microstructure.  This integration enables comprehensive tracking of software provenance, ensuring transparency and accountability throughout the software lifecycle.

Diagram:

graph TD

    subgraph MDATS["MDATS (Patent 17)"]

        subgraph Digital_Audit_Trail["Digital&nbsp;Audit&nbsp;Trail&nbsp;(Patents&nbsp;13,&nbsp;15)"]

            Events["System Events (Governance, Software, Security)"] --> DLT["Decentralized Ledger<br>(Patents 13, 15)"]

            DLT --> Audit_Logs["Digital Audit Logs"]

        end

        subgraph Physical_Audit_Trail["Physical&nbsp;Audit&nbsp;Trail&nbsp;(Patent&nbsp;14)"]

            Events --> Microstructure_Gen["Microstructure Generator (Patent 14)"]

            Microstructure_Gen --> Microstructures["3D Microstructures"]

        end

        subgraph Correlation_Analysis["Correlation & Analysis"]

            AI_Analyzer["AI-Driven Analyzer"] --> Anomaly_Detection["Anomaly Detection"]

            Audit_Logs --> AI_Analyzer

            Microstructures --> AI_Analyzer

            Microstructure_Verifier["Microstructure Verifier"] --> AI_Analyzer

        end

        Digital_Audit_Trail ----> Correlation_Analysis <---- Physical_Audit_Trail

    end

    subgraph External_Auditors["External Auditors"]

        Auditor_1["Auditor 1"]

        Auditor_N["... Auditor N"]

    end

    MDATS -->|Secure Access| External_Auditors

Description of Diagram:

This diagram illustrates the components and functionalities of the Multi-Dimensional Audit Trail System (MDATS) as described in Patent 17, emphasizing its multi-faceted approach to auditing.

  1. MDATS (Patent 17): This subgraph encapsulates the entire MDATS and its internal components.

  1. Digital Audit Trail (Patents 13, 15):  This subgraph represents the digital aspect of the audit trail.

  1. Physical Audit Trail (Patent 14): This subgraph represents the physical aspect of the audit trail.
  1. Correlation & Analysis: This subgraph represents the core of the MDATS, where the digital and physical audit trails are correlated and analyzed.
  1. External Auditors: This subgraph represents authorized external auditors who can securely access the MDATS for independent verification.

Key Features and Interactions:

This diagram effectively communicates the innovative aspects of the MDATS and its importance within the ASKA ecosystem.  It visually represents the key components and their interactions, emphasizing the multi-dimensional approach, security features, and transparency provided by the system.

Claims:

  1. Multi-Dimensional Audit Trail System (MDATS) for ASKA:

  1. AI-Driven Microstructure Analysis for Enhanced Auditing:

  1. Microstructure-Assisted Vote Verification:

  1. Software Provenance Tracking with Microstructure Correlation:

  1. Blockchain-Based Software Provenance Ledger:

  1. Secure Microstructure-to-Ledger Linkage:

  1. Real-Time Anomaly Reporting and Alerting:

  1. Decentralized Auditing and Verification:

Patent Group VI. Secure Collaboration and Data Management

Patent 18: Secure and Adaptive Hyper-Virtualization System for Collaborative Workloads with Decentralized Policy Management, Real-time Security Monitoring, and Privacy-Preserving Data Sharing

Abstract:

This invention discloses a Secure Hyper-Virtualization System (SHVS) for a secure, multi-kernel, zoned computing environment, enabling secure collaboration and controlled data sharing between Modular Isolated Execution Stacks (IES).  The SHVS establishes and manages collaboration contexts using dynamically configurable capabilities, defining fine-grained access rights and permissions for participating IES instances within and across Zones. A Secure Communication Agent establishes and manages multiple secure communication paths, leveraging a multi-channel network, quantum-resistant communication, and an adaptive routing mechanism optimized for performance, security, and fault tolerance in collaborative operations.  A novel decentralized policy management system enables individual zones or IES instances to define and enforce their own collaboration policies, utilizing a distributed consensus protocol for conflict resolution.  Real-time security monitoring, incorporating a hierarchical security mesh and AI-powered anomaly detection, detects and responds to security violations, dynamically adjusting access control and communication policies.  Furthermore, the SHVS integrates Secure Data Enclaves for privacy-preserving data sharing and analytics during collaboration, leveraging techniques such as differential privacy, homomorphic encryption, and secure multi-party computation. This integrated, capability-based approach ensures secure, efficient, and adaptable collaboration for dynamic and sensitive workloads in a decentralized environment.

Diagram:

graph LR

    subgraph "Secure Hyper-Virtualization System (SHVS)"

        direction LR

        subgraph "Zone Hierarchy"

            Zone_Root["Root Zone"]

            Zone_1["Zone 1<br>(e.g., Enterprise Network)"]

            Zone_2["Zone 2<br>(e.g., Cloud Provider)"]

            Zone_3["Zone 3<br>(e.g., Partner Organization)"]

            Zone_Root --> Zone_1

            Zone_Root --> Zone_2

            Zone_Root --> Zone_3

        end

        subgraph "Collaboration Context Management"

            Context_Creation["Context Creation<br>(Distributed Consensus)"]

            Context_Modification["Context Modification"]

            Context_Termination["Context Termination"]

            Context_Creation --> Collaboration_Context

            Context_Modification --> Collaboration_Context

            Context_Termination --> Collaboration_Context

            Policy_Engine["Security Policy Engine<br>(Zone-Specific Policies)"] --> Context_Creation

            Policy_Engine --> Context_Modification

            Policy_Engine --> Context_Termination

            Trust_Mgmt["Trust Management<br>(DTMS Integration)"] --> Context_Creation

            Trust_Mgmt --> Context_Modification

            Trust_Mgmt --> Context_Termination

        end

        subgraph "Collaboration Context"

            Collaboration_Context["Collaboration Context<br>(Defines Participants, Data Access, Security Policies)"]

            IES_A["IES Instance A"] --> Collaboration_Context

            IES_B["IES Instance B"] --> Collaboration_Context

            IES_C["IES Instance C"] --> Collaboration_Context

            Data_Sanitization["Data Sanitization & Transformation"] --> Collaboration_Context

            Secure_Communication["Secure Inter-IES Communication<br>(Unidirectional Channels, Patent 2)"] --> Collaboration_Context

            Monitoring["Real-Time Monitoring & Auditing<br>(Tamper-proof Log)"] --> Collaboration_Context

        end

        subgraph "Data Federation & Secure Data Exchange"

            Data_Federation["Data Federation<br>(Cross-Zone Data Sharing)"]

            Secure_Data_Exchange["Secure Data Exchange<br>(Privacy-Preserving Techniques)"]

            Data_Federation --> Collaboration_Context

            Secure_Data_Exchange --> Collaboration_Context

            Secure_Data_Exchange --> Data_Federation

        end

        subgraph "Dynamic Adaptation"

            Dynamic_Adaptation["Dynamic Adaptation<br>(Policy Adjustments, Context Termination)"]

            Dynamic_Adaptation --> Collaboration_Context

            Workload_Mgmt["Workload Management"] --> Dynamic_Adaptation

            Security_Assessment["Security Assessment"] --> Dynamic_Adaptation

            Governance_Decisions["Governance Decisions"] --> Dynamic_Adaptation

        end

    end

    style Collaboration_Context fill:#ccf,stroke:#888,stroke-width:2px

    style Secure_Communication fill:#ccf,stroke:#888,stroke-width:2px

    style Data_Sanitization fill:#ccf,stroke:#888,stroke-width:2px

    style Monitoring fill:#ccf,stroke:#888,stroke-width:2px

Description of Diagram:

This detailed diagram illustrates the internal workings of the Secure Hyper-Virtualization System (SHVS) and its key components. It focuses on the management of collaboration contexts, data exchange, security policies, and dynamic adaptation.

Patent References:

This detailed diagram provides a comprehensive visualization of the SHVS's architecture, highlighting its key components, functionalities, and how it enables secure and scalable collaboration across ASKA deployments. The emphasis on security and dynamic adaptation underscores the SHVS’s ability to accommodate evolving requirements and maintain a robust security posture.

Claims:

  1. A secure hyper-virtualization system (SHVS) for a computing system comprising a plurality of Modular Isolated Execution Stacks (IES) (Patent 1) organized into a hierarchy of Zones (Patent 18), each Zone associated with a Trust Root Configuration (TRC) stored on a decentralized, tamper-proof ledger (Patent 15), and a secure communication mechanism (Patent 2) between said IES instances, wherein said SHVS enables secure collaboration and controlled data sharing between authorized IES instances within and across Zones, comprising:

a. a Collaboration Context Management module that establishes and manages collaboration contexts, wherein:

    i. each context defines a set of participating IES instances identified by unique, cryptographically verifiable identifiers (Patent 4), permitted actions authorized by dynamically configurable capabilities (Patent 2), data access policies governed by trust policies and resource borrowing rules defined in said TRCs, and inter-IES communication rules enforced by the Secure Communication Agent;

    ii. collaboration contexts are established based on mutual consent of participating IES instances or Zones, using a consent protocol and requiring agreement on shared collaboration policies, expressed using a declarative language; and

    iii. the parameters and policies of each collaboration context are dynamically adjusted in real-time based on feedback from the Real-Time Security Monitoring module, changes in trust levels of participating IES instances, resource availability, and policy updates from the DTMS (Patent 4);

b. a Secure Communication Agent (SCA) (Patent 2, Patent 3) that dynamically discovers, selects, establishes, and manages multiple secure communication paths between said participating IES instances, wherein said SCA:

    i. utilizes dynamically configurable capabilities (Patent 2), a multi-channel network (Patent 3), and quantum-resistant communication (Patent 5) to enhance availability, fault tolerance, and security;

    ii. selects communication paths based on path availability, performance metrics (latency, bandwidth), trust levels of intermediate nodes and zones, and dynamically reconfigurable capabilities;

    iii. incorporates hardware-enforced unidirectional communication channels (Patent 2) to ensure strict data flow control for highly sensitive data; and

    iv. monitors the status, availability and performance of established communication paths, automatically detecting and removing failed or degraded paths, and dynamically rerouting traffic to maintain communication integrity and optimize performance during collaborative operations; and

c. a Real-Time Security Monitoring module, integrated with a hierarchical security mesh (Patent 2) and an AI-powered anomaly detection system (Patent 7), that continuously monitors collaborative operations within each context for security violations, dynamically adjusts access control policies based on detected threats, trust levels, resource availability, and policy updates from said DTMS, and transmits authenticated security reports using a secure communication protocol (Patents 2, 3).

  1. The system of claim 1, wherein said SHVS integrates with a Secure Resource Borrowing Mechanism (Patent 9) and said DTMS (Patent 4) to enable secure and dynamic sharing of idle resources between said IES instances within a collaboration context, wherein:

    a. resource allocation is managed by said DTMS based on trust policies defined in said TRCs, dynamically configurable capabilities, real-time resource availability, and resource requests encoded within hop fields; and

    b. resource borrowing requests are prioritized using a queuing mechanism (Patent 3) that prioritizes requests from trusted IES instances or zones based on their trust levels, enhancing efficiency and preventing denial-of-service attacks targeting resource borrowing.

  1. The system of claim 1, wherein said SHVS incorporates Secure Data Enclaves (Patent 20) and Privacy-Preserving Data Exchange Protocols (Patent 22) to enable privacy-preserving collaborative data analytics within a collaboration context, utilizing at least one of: Differential Privacy, Homomorphic Encryption, or Secure Multi-Party Computation (MPC), wherein the selection and configuration of said protocols are dynamically adjusted based on the sensitivity of the data being exchanged, trust levels between collaborating IES instances, and policies defined in said TRCs.

  1. The system of claim 1, wherein:

    a. collaboration contexts are governed by Zone-specific policies defined in the relevant TRCs, allowing for differentiated collaboration rules and access control restrictions based on Zone membership;

    b. said Collaboration Context Management module enforces access control policies using dynamically issued and managed capabilities (Patent 2) that define permitted actions and accessible address ranges for each participating IES instance within a collaboration context, and automatically revokes or restricts capabilities in response to security violations or changes in trust levels;

    c. said IES instances can dynamically join or leave collaboration contexts based on authorization granted by said Collaboration Context Management module, subject to trust policies defined in the relevant TRCs and using a secure, authenticated handshake protocol; and

    d. a secure resource discovery mechanism, utilizing authenticated control messages (Patent 2) and a distributed resource directory, allows IES instances within a collaboration context to discover and interact with authorized resources and services offered by other participating IES instances.

    e. data sanitization and transformation mechanisms protect sensitive information during inter-IES collaborations within a context, including anonymization, aggregation, and format transformation before data is shared between instances.

    f. a tamper-proof audit log records relevant events and data exchanges, including timestamps, participating IES instance identifiers, and actions performed, for all collaborative operations within each context.

  1. The system of claim 1, wherein multiple Zones can be federated to create larger, interconnected collaboration domains, subject to trust relationships established between participating Zones, as defined by their TRCs and managed by the DTMS, and wherein inter-zone collaboration policies are negotiated and agreed upon using a distributed consensus mechanism (Patent 13), promoting secure and flexible collaboration across multiple zones.

  1. The system of claim 5, wherein the zone hierarchy abstracts the physical location and network configuration of ASKA deployments, enabling seamless collaboration between IES instances regardless of their physical separation.

  1. The system of claim 1, wherein said SHVS supports dynamic adaptation and optimization of collaboration contexts and communication paths, based on at least one of: real-time workload demands, security assessments from the security monitoring module, dynamic trust metric updates from the DTMS, or changes in zone-specific policies, enhancing the efficiency, adaptability, and security of cross-IES collaborations.

Patent 19: Privacy-Preserving Federated Learning System using Secure Multi-Party Computation across Isolated Execution Stacks

Abstract:

This invention presents a Privacy-Preserving Federated Learning System for ASKA that enables collaborative machine learning across physically isolated Modular Isolated Execution Stacks (IES) while upholding data privacy and security. The system utilizes Secure Multi-Party Computation (MPC) to aggregate model updates from participating IES instances without directly exchanging raw data, ensuring that sensitive information remains protected within its respective isolated environment.

The Federated Learning System incorporates a dynamic orchestrator that adapts model selection, aggregation strategies, and privacy-preserving techniques based on the characteristics of the data and the desired security level.  By leveraging ASKA's hardware-enforced isolation, unidirectional communication, and hierarchical zone management, this invention provides a robust and scalable framework for secure and collaborative machine learning in privacy-sensitive domains.

Diagram:

graph TD

    subgraph IES_Instances["IES Instances (Data & Local Models)"]

        IES_1["IES 1"]

        IES_2["IES 2"]

        IES_N["... IES N"]

        subgraph IES_1_Details["IES 1 (Expanded)"]

            Data_1["Local Data 1"] --> Model_1["Local Model 1"]

            Model_1 --> Updates_1["Model Updates 1"]

            Updates_1 --> Encryptor["Encryptor (Patent 5/24)"]

        end

        IES_1 --> IES_1_Details

        subgraph IES_2_Details["IES 2 (Expanded)"]

            Data_2["Local Data 2"] --> Model_2["Local Model 2"]

            Model_2 --> Updates_2["Model Updates 2"]

            Updates_2 --> Encryptor

        end

        IES_2 --> IES_2_Details

        Updates_N["Model Updates N"] --> Encryptor

    end

    Encryptor --> MPC["Secure Multi-Party Computation (MPC)"]

    MPC --> Decryptor["Decryptor (Patent 5/24)"]

    Decryptor --> Aggregated_Model["Aggregated Model"]

    Aggregated_Model -- Distribution --> IES_Instances

    subgraph Federated_Learning_Orchestrator["Federated Learning Orchestrator"]

        Model_Selection["Model Selection"]

        Aggregation_Strategy["Aggregation Strategy"]

        Privacy_Techniques["Privacy Techniques"]

        Model_Selection --> Aggregation_Strategy

        Aggregation_Strategy --> Privacy_Techniques

        Privacy_Techniques --> MPC

        IES_Instances -.- Model_Selection

        Metrics["Performance Metrics"] --> Model_Selection

    end

    DTMS["Dynamic Trust Management<br>(Patent 4)"] -->|Trust Level| Federated_Learning_Orchestrator

    style MPC fill:#ccf,stroke:#888,stroke-width:2px

    style Federated_Learning_Orchestrator fill:#aaf,stroke:#444

Description of Diagram:

This diagram illustrates the Privacy-Preserving Federated Learning System using Secure Multi-Party Computation (MPC) across Isolated Execution Stacks (IES).

  1. IES Instances (Data & Local Models): This subgraph represents the distributed nature of the system, with each IES instance holding its own local data and model.  Two IES instances are expanded to show internal details.  Data flow within each IES instance is shown, including data encryption before transmission to the MPC engine.

  1. Secure Multi-Party Computation (MPC): This component performs the secure aggregation of model updates from the IES instances, preserving privacy.  Encrypted updates are sent to this component.

  1. Aggregated Model: This represents the combined model generated by the MPC process.  This model is then distributed back to the IES instances.

  1. Federated Learning Orchestrator: This subgraph manages the federated learning process.

Key Features and Interactions:

This diagram clearly visualizes the key innovations of Patent 19 and how it enables privacy-preserving federated learning within the ASKA architecture. It clarifies the interactions between components and emphasizes the system's dynamic and secure nature.

Claims:

  1. Federated Learning Across Isolated Execution Stacks:

  1. Secure Multi-Party Computation for Model Aggregation:

  1. Hardware-Enforced Isolation for Secure Computation:

  1. Dynamic Model Selection and Aggregation Strategies:

  1. Adaptive Privacy-Preserving Techniques:

  1. Zone-Aware Federated Learning for Scalable Collaboration:

  1. AI-Driven Model Optimization and Personalization:

  1. Blockchain-Based Model Provenance and Integrity Tracking:

Patent 20: Secure Data Enclave System with Privacy-Preserving Collaborative Data Analytics

Abstract:

This invention presents a Secure Data Enclave System for ASKA, enabling secure and privacy-preserving collaborative data analytics across physically isolated Modular Isolated Execution Stacks (IES).  By leveraging the inherent security features of ASKA's hardware-enforced isolation, unidirectional communication, and dynamic trust management, the system allows multiple IES instances, each hosting a secure data enclave, to engage in joint data analysis tasks without compromising data confidentiality or integrity.

The system incorporates a suite of privacy-preserving data sharing mechanisms, including differential privacy, homomorphic encryption, and Secure Multi-Party Computation (MPC), to protect sensitive information during data ingestion, analysis, and inter-enclave communication.  The Secure Data Enclave System provides a robust and scalable solution for collaborative data analytics in privacy-sensitive domains, such as healthcare, finance, and government, facilitating data sharing and insights generation while upholding stringent security and privacy standards.

Diagram:

graph TD

    subgraph Zone_A["ASKA Zone A"]

        IES_A["IES Instance A (Enclave A)"]

        Data_A["Data Source A"] --> IES_A

    end

    subgraph Zone_B["ASKA Zone B"]

        IES_B["IES Instance B (Enclave B)"]

        Data_B["Data Source B"] --> IES_B

    end

    subgraph "ASKA Hub"

        Hub["Hub"]

        IES_A --> Hub

        IES_B --> Hub

        Policy["Policy Engine (Patent 4)"] --> Hub

        DTMS["DTMS (Patent 4)"] --> Hub

    end

    subgraph Shared_Analysis_Env["Shared&nbsp;Analysis&nbsp;Environment&nbsp;(Secure)"]

        IES_A -- Secure Communication (Patents 2, 3, 5) --> SAE["Secure Analysis Engine"]

        IES_B -- Secure Communication (Patents 2, 3, 5) --> SAE

        SAE --> Results["Analysis Results"]

       

        subgraph Privacy_Preserving_Mechanisms["Privacy-Preserving Mechanisms"]

            DP["Differential Privacy"]

            HE["Homomorphic Encryption"]

            MPC["Secure Multi-Party Computation (Patent 19)"]

        end

        SAE --> Privacy_Preserving_Mechanisms

        Privacy_Preserving_Mechanisms --> Results

    end

    Zone_A ----> Shared_Analysis_Env <---- Zone_B

    Ledger["Decentralized Ledger (Patents 13, 15)"]-.->|Audit Trail| Shared_Analysis_Env

    style SAE fill:#ccf,stroke:#888,stroke-width:2px

Description of Diagram:

  1. ASKA Zones: Two zones, A and B, are shown, each with an IES instance hosting a Secure Data Enclave. Each enclave receives data from its respective data source. This setup visualizes the collaborative aspect across different data sources and zones.  The IES instances communicate securely with the Hub, influenced by policy decisions from the policy engine and the state of DTMS.

  1. Shared Analysis Environment: This central subgraph depicts the secure environment where collaborative analysis occurs.

  1. Secure Communication:  The connections between IES instances and the SAE utilize ASKA's secure communication channels (Patents 2, 3, and 5), including data diodes and quantum-resistant encryption.

Key Features and Interactions:

Claims:

  1. Secure Data Enclave System for ASKA:

  1. Hardware-Enforced Isolation for Data Enclaves:

  1. Secure Data Ingestion and Access Control:

  1. Privacy-Preserving Data Sharing and Transformation:

  1. Collaborative Data Analytics within Secure Enclaves:

  1. Dynamic Enclave Configuration and Policy Management:

  1. AI-Driven Data Analysis and Insights Generation:

  1. Zone-Aware Data Enclave Federation:

  1. Blockchain-Based Data Provenance and Auditability:

  1. Secure Data Enclave as a Service (SDEaaS):

Patent 21: Blockchain-Enabled Self-Evolving Software System with 3D-Printed Microstructure Provenance Tracking and AI-Driven Security

Abstract:

This invention presents a Blockchain-Enabled Self-Evolving Software System for ASKA that ensures the secure and verifiable evolution of software within a physically isolated, multi-kernel computing environment.  The system combines an AI-driven Automated Evolutionary Software Development System (AESDS) with a 3D-printed microstructure audit trail and a blockchain-based provenance ledger. Each software update generated by the AESDS is linked to a unique, tamper-evident microstructure, providing a physical representation of the software's integrity and a permanent record of its development history.

The blockchain ledger anchors the microstructure identifiers and validation records, enabling transparent and auditable tracking of software provenance. AI-driven security mechanisms continuously monitor system behavior and automatically generate security updates, each linked to a corresponding microstructure for verification.  This multi-dimensional approach ensures secure, adaptable, and trustworthy software development within the ASKA ecosystem, setting a new standard for transparency, accountability, and security in critical systems.

Diagram:

graph TD

    subgraph AESDS["AESDS&nbsp;(Automated&nbsp;Evolutionary&nbsp;Software&nbsp;Development&nbsp;System&nbsp;Patent&nbsp;16)"]

        AI_Engine["AI Engine (Code Gen & Refinement)"] --> Code_Generator["Code Generator"]

        KB["Knowledge Base"] --> AI_Engine

        Metrics["Performance Metrics<br>(from IES)"] --> AI_Engine

        User_Feedback["User Feedback"] --> AI_Engine

        Threat_Intel["Threat Intelligence"] --> AI_Engine

        Code_Generator --> Validator["Validator (Sandbox - Patent 1)"]

        Validator --> Software_Update["Software Update"]

        Adaptive_Security["Adaptive Security<br>(Patent 7)"] --> AI_Engine

    end

    Software_Update --> Signer["Digital Signer (Patent 24)"]

    Signer --> Microstructure_Gen["Microstructure Generator (Patents 14, 17)"]

    Microstructure_Gen --> Microstructure["3D Microstructure"]

    Signer --> Blockchain["Blockchain Ledger<br>(Patents 13, 15)"]

    Microstructure --> Blockchain

    Blockchain --> Deployer["Secure Deployer<br>(DTMS - Patent 4)"]

    Deployer --> IES_Instances["IES Instances (Patent 1)"]

    IES_Instances --> Metrics

    subgraph ASKA_Hub

        Hub["Hub"] --> AESDS

    end

    style Microstructure fill:#ccf,stroke:#888,stroke-width:2px

    style Blockchain fill:#aaf,stroke:#444

Description of Diagram:

This diagram for Patent 21 (Blockchain-Enabled Self-Evolving Software System) clarifies the data flow, integrates relevant ASKA components, and emphasizes the security aspects of software evolution.

  1. AESDS (Patent 16): This subgraph represents the core of the automated software development process.

  1. ASKA Hub:  Now explicitly included. The Hub provides triggers for software updates (e.g., based on scheduled updates, vulnerability reports, or governance decisions) and/or policy guidelines for the AESDS.  This highlights the managed aspect of the automated system.

  1. Linking to Blockchain and Microstructures:

  1. Secure Deployment:
  1. Feedback Loop:  Performance Metrics from updated IES instances are fed back into the AESDS for continuous improvement.

Claims:

  1. Blockchain-Enabled Self-Evolving Software System:

  1. Microstructure-Based Software Provenance Tracking:

  1. Blockchain-Anchored Microstructure Records:

  1. AI-Driven Security with Microstructure Correlation:

  1. Multi-Layered Validation with Sandboxed Environments:

  1. Decentralized Governance with Microstructure Verification:

  1. User Feedback Integration for Adaptive Software Evolution:

  1. Dynamic Software Rollback with Microstructure Verification:

  1. Secure Remote Software Deployment and Verification:

  1. Open-Source Microstructure Verification Tools:

Patent 22: Secure Inter-Zone Collaboration Framework with Privacy-Preserving Data Exchange and Distributed Ledger Synchronization

Abstract:

This invention introduces a Secure Inter-Zone Collaboration Framework (SIZCF) that enables secure and controlled data sharing and collaborative operations between ASKA deployments operating across a hierarchy of zones. The SIZCF leverages ASKA's foundational security principles, including hardware-enforced isolation, unidirectional communication, and dynamic trust management, to extend these safeguards to inter-zone interactions.

The framework incorporates privacy-preserving data exchange protocols, such as differential privacy, homomorphic encryption, and Secure Multi-Party Computation (MPC), to protect sensitive information during collaborative data analysis and cross-zone data sharing. A distributed ledger synchronization mechanism ensures data consistency and integrity across the ledgers of collaborating zones. The SIZCF empowers ASKA deployments to securely collaborate and share data at scale, facilitating secure and trustworthy interactions between organizations, government agencies, or geographically distributed systems, while upholding stringent security and privacy standards.

Diagram:

graph TD

    subgraph Zone_A["ASKA Zone A"]

        IES_A1["IES A1"]

        IES_A2["... IES An"]

        Hub_A["ASKA Hub A"]

        Ledger_A["Decentralized Ledger A"]

        IES_A1 --> Hub_A

        IES_A2 --> Hub_A

        Hub_A --> Ledger_A

    end

    subgraph Zone_B["ASKA Zone B"]

        IES_B1["IES B1"]

        IES_B2["... IES Bn"]

        Hub_B["ASKA Hub B"]

        Ledger_B["Decentralized Ledger B"]

        IES_B1 --> Hub_B

        IES_B2 --> Hub_B

        Hub_B --> Ledger_B

    end

    subgraph SIZCF["SIZCF (Patent 22)"]

        direction LR

        ZD["Zone Discovery"]

        TA["Trust Assessment<br>(Patent 4)"]

        SCA["Secure Communication<br>Agent (Patents 3, 5)"]

        PDS["Privacy-Preserving<br>Data Sharing (Patent 20)"]

        Ledger_Sync["Distributed Ledger<br>Synchronization"]

        ZD --> TA

        TA --> SCA

        SCA --> PDS

        PDS --> Ledger_Sync

    end

    Hub_A ----> SIZCF <---- Hub_B

    Ledger_A ----> Ledger_Sync <---- Ledger_B

    subgraph External_Systems["External Systems/Zones"]

        Zone_C["Zone C"]

    end

    SIZCF ----> Zone_C

Description for Diagram:

This diagram illustrates the Secure Inter-Zone Collaboration Framework (SIZCF) and its role in enabling secure communication and data sharing between different ASKA Zones.

  1. ASKA Zone A & Zone B:  These subgraphs represent two independent ASKA zones, each with its own IES instances, ASKA Hub, and Decentralized Ledger. This emphasizes the decentralized nature of ASKA.

  1. SIZCF (Patent 22): This subgraph represents the core components of the SIZCF.  The components are arranged left-to-right to visualize the typical flow of a collaboration request.

  1. Connections and Data Flow:

Key Features Illustrated:

Claims:

  1. A secure inter-zone collaboration framework (SIZCF) for a computing system comprising a plurality of ASKA deployments (Patent 17), each deployment having a set of Modular Isolated Execution Stacks (IES) (Patent 1) and operating across a hierarchy of Zones (Patent 18), each Zone associated with a Trust Root Configuration (TRC) stored on a decentralized, tamper-proof ledger (Patent 15), wherein said SIZCF enables secure communication, data sharing, and collaborative operations between authorized IES instances located in different Zones, comprising:

a) a Zone Discovery mechanism for: i) identifying participating Zones; and ii) authenticating participating Zones based on their TRCs;

b) a Trust Assessment mechanism (Patent 4) for establishing trust relationships between Zones based on at least one of: i) their respective TRCs; ii) real-time security assessments; or iii) historical behavior;

c) a Secure Communication Agent (Patents 2, 3) for: i) establishing and managing multiple secure communication paths between Zones; ii) utilizing a multi-channel network (Patent 3) and quantum-resistant communication (Patent 5); and iii) dynamically selecting communication paths based on path availability, performance metrics, and trust levels;

d) Privacy-Preserving Data Exchange Protocols (Patent 20) for protecting sensitive information during inter-zone collaborations, including at least one of: i) Differential Privacy; ii) Homomorphic Encryption; or iii) Secure Multi-Party Computation (MPC); and

e) a Distributed Ledger Synchronization mechanism for: i) ensuring data consistency and integrity across the decentralized ledgers of collaborating Zones; and ii) utilizing a distributed consensus protocol (Patent 13) for secure and consistent ledger updates.

  1. The system of claim 1, wherein said Zone Discovery mechanism utilizes a decentralized directory service for discovering and registering participating Zones, wherein each Zone's registration information includes its TRC and other relevant metadata.

  1. The system of claim 1, wherein said Trust Assessment mechanism integrates with the DTMS (Patent 4) to dynamically adjust trust levels between Zones based on real-time security assessments, historical behavior, and adherence to trust policies defined in the relevant TRCs.

  1. The system of claim 1, wherein said Secure Communication Agent:

a) utilizes dynamically configurable capabilities (Patent 2) to control access to resources and services across Zones; and b) encrypts inter-zone communication using quantum-resistant encryption (Patent 5) and hardware-enforced unidirectional communication channels (Patent 2) where appropriate.

  1. The system of claim 1, wherein said Privacy-Preserving Data Exchange Protocols are dynamically selected and configured based on the sensitivity of the data being exchanged and the trust levels between collaborating Zones.

  1. The system of claim 1, wherein said Distributed Ledger Synchronization mechanism:

a) selectively synchronizes only relevant data and events between collaborating Zones, minimizing communication overhead and storage requirements; and b) provides a tamper-proof audit trail of all inter-zone data exchanges and collaborative operations.

Patent Group VII. Miscellaneous

Patent 23: Adaptive Context-Aware MFA with Biometric and Behavioral Analysis

Abstract:

This invention enhances ASKA's Multi-Factor Authentication (MFA) capabilities by introducing an adaptive, context-aware system that integrates biometric and behavioral analysis with out-of-band token verification.  Leveraging the existing IES and multi-channel architecture (Patents 1, 2, 3, and 8), this system dynamically adjusts authentication requirements based on real-time risk assessments derived from user behavior, environmental factors, and threat intelligence. Biometric authentication methods, integrated with the Secure UI Kernel (Patent 11), provide an additional layer of security. The system utilizes Secure Multi-Party Computation (MPC) for privacy-preserving analysis of biometric data, ensuring user privacy while enhancing authentication accuracy.  A decentralized ledger records all authentication events, providing a transparent and auditable trail. This adaptive approach strengthens ASKA's security posture by tailoring authentication rigor to the specific context of each access request.

Diagram 1:

graph LR

    subgraph "ASKA Endpoint"

        direction LR

        User --> Secure_UI_Kernel["Secure UI Kernel (Patent 11)<br>Biometric Capture"]

        Secure_UI_Kernel --> IES["IES Instance (Patent 1)"]

    end

    subgraph "ASKA Backend"

        direction LR

        IES --> Context_Engine["Context Engine<br>(Behavior Analysis,<br>Risk Assessment)"]

        Threat_Intel["Threat Intelligence Feeds"] --> Context_Engine

        Env_Factors["Environmental Factors"] --> Context_Engine

        Context_Engine --> Auth_Policy["Authentication Policy Engine"]

        Auth_Policy --> MFA_Token["Out-of-Band MFA Token<br>(Patent 8)"]

        MFA_Token --> Data_Diode["Data Diode (Patent 2)"]

        Data_Diode --> Network_Channel["Network Channel (Patent 3)"]

        IES --> MPC_Engine["MPC Engine<br>(Biometric Verification)"]

        MPC_Engine --> Auth_Policy

        subgraph "Decentralized Ledger (Patents 13 & 15)"

            Auth_Policy --> Ledger["Authentication Events"]

            MPC_Engine --> Ledger["Verification Results"]

        end

    end

    Network_Channel --> External_Service["External Service/Resource"]

    style Context_Engine fill:#ccf,stroke:#888,stroke-width:2px

    style Auth_Policy fill:#aaf,stroke:#666,stroke-width:1px

    style MPC_Engine fill:#ccf,stroke:#888,stroke-width:2px

Diagram 1 Description:

This Merlin diagram illustrates the data flow and component interactions within the Adaptive Context-Aware MFA system.

Diagram 2:

graph TD

    subgraph "ASKA&nbsp;Endpoint&nbsp;(Patents&nbsp;1,&nbsp;11)"

        User --> UI["Secure UI<br>(Patent 11)"]

        UI --> Biometric_Capture["Biometric Capture"]

        Biometric_Capture --> IES["IES Instance<br>(Patent 1)"]

        IES -->|"Encrypted Biometric Data"| Biometric_Vault["Biometric Vault<br>(Patent 23)"]

    end

    Biometric_Vault -- "Secure Channel (Patent 3)" --> Authentication_Server["Authentication Server"]

    Authentication_Server -- "Verification Result" --> IES

    subgraph "Authentication Server"

        ZK_Matcher["Zero&nbsp;Knowledge&nbsp;Biometric&nbsp;Matcher (Patent&nbsp;6)"]

        Template_Database["Encrypted Biometric<br>Template Database"]

        ZK_Matcher --> Template_Database

        Biometric_Vault --> ZK_Matcher

    end

Description of Diagram 2:

Diagram Description for Patent 23 (Adaptive Context-Aware MFA):

This diagram focuses specifically on the biometric authentication aspect of the Adaptive Context-Aware MFA system described in Patent 23, highlighting the secure handling and verification of biometric data.

ASKA Endpoint (Patents 1, 11): This subgraph represents the user interaction and initial biometric data handling within the secure endpoint.

User: Initiates the authentication process.

Authentication Server: This component houses the core biometric verification logic.

Zero-Knowledge Biometric Matcher (Patent 6): This component performs the biometric matching against stored templates without exposing the raw biometric data, utilizing zero-knowledge proofs.

Encrypted Biometric Template Database: Stores the encrypted biometric templates for registered users.

Data Flow and Connections: The arrows indicate the flow of data and control information:

Patent Integration: The diagram clearly indicates the integration with:

Key Features Highlighted:

Diagram 3:

graph TD

    subgraph ASKA_Endpoint["ASKA Endpoint"]

        User --> UI["Secure UI (Patent 11)"]

        UI --> Biometrics["Biometric Capture"]

        UI --> Behavior["Behavioral Analysis"]

        Biometrics --> IES["IES Instance (Patent 1)"]

        Behavior --> IES

    end

    IES --> Secure_Channel["Secure Channel (Patent 3)"]

    subgraph ASKA_Hub["ASKA Hub"]

        Secure_Channel --> Context_Engine["Context Engine"]

        Threat_Intel["Threat Intelligence"] --> Context_Engine

        Context_Engine --> Policy_Engine["Authentication Policy Engine"]

        Policy_Engine -- Low Risk --> Access_Granted["Access Granted"]

        Policy_Engine -- High Risk --> MFA["MFA Challenge"]

        subgraph MFA_System["MFA System (Patent 8)"]

            MFA --> Token_Gen["Token Generator"]

            Token_Gen --|Out-of-Band|--> User

            User -->|MFA Token| MFA

        end

       

        User -->|Enters Token| UI

        UI -->|Token Verification| IES

        IES --|Verified|--> Access_Granted

        subgraph "Decentralized&nbsp;Ledger&nbsp;(Patents&nbsp;13,&nbsp;15)"

            Ledger["Ledger"]

            Policy_Engine --> Ledger

            IES --> Ledger

        end

    end

    Access_Granted --> Resource["Protected Resource"]

Description for Diagram 3:

  1. ASKA Endpoint:

  1. Secure Channel (Patent 3):  The IES communicates with the ASKA Hub through a secure channel, emphasizing the protection of sensitive authentication data.

  1. ASKA Hub:

  1. Protected Resource: The user gains access to the protected resource upon successful authentication.

Claims:

  1. Adaptive Context-Aware MFA System: A secure computing system comprising a plurality of Modular Isolated Execution Stacks (IES) (Patent 1) with physically segregated network channels (Patent 3), an out-of-band MFA token mechanism (Patent 8), and a Secure UI Kernel (Patent 11).  An adaptive context-aware MFA system dynamically adjusts authentication requirements based on real-time risk assessments derived from:

  1. Privacy-Preserving Biometric Authentication: The system of claim 1, wherein biometric authentication data is collected and processed using Secure Multi-Party Computation (MPC). This ensures that raw biometric data remains within the isolated environment of the IES, preventing unauthorized access while enabling accurate and private biometric verification.

  1. Behavioral Biometric Analysis: The system of claim 1, wherein user behavior analysis includes continuous monitoring of user interactions within the Secure UI Kernel.  Deviations from established behavioral baselines trigger dynamic adjustments to authentication requirements, enhancing security against compromised or impersonated users.

  1. Decentralized Audit Trail: The system of claim 1, wherein all authentication events, including risk assessments, authentication challenges, and biometric verification results, are recorded on a decentralized, tamper-proof ledger (Patents 13 & 15). This provides a transparent and auditable record of all authentication activities, facilitating investigation and accountability.

  1. Integration with Threat Intelligence Feeds: The system of claim 1, wherein the context-aware MFA system integrates with real-time threat intelligence feeds.  Elevated threat levels trigger increased authentication rigor, proactively mitigating potential attacks.

Patent 24: Hardware-Enforced Secure Encrypted Enclave for Data at Rest (HESE-DAR) with Dynamic Resource Allocation and Decentralized Governance Integration

Abstract:

This invention discloses a Hardware-Enforced Secure Encrypted Enclave for Data at Rest (HESE-DAR) designed for secure computing endpoints utilizing Modular Isolated Execution Stacks (IES). The HESE-DAR provides dedicated, encrypted storage for each IES instance, performing encryption and decryption operations in isolated hardware.  Granular access control policies enforced within the HESE-DAR restrict access to encrypted data based on user identity, application context, and predefined access control lists.  Dynamic resource allocation within the HESE-DAR optimizes storage utilization for each IES instance based on real-time demands.  Integration with a Dynamic Trust Management System (DTMS) enables secure sharing of encrypted data between trusted IES instances, while a 3D-printed microstructure system generates a tamper-evident audit trail for key management operations, enhancing security and accountability. The HESE-DAR further incorporates anti-tamper mechanisms and post-quantum cryptographic algorithms, providing robust protection against physical and software-based attacks, including threats from quantum computers.  This comprehensive approach safeguards data at rest, complementing ASKA's existing protections for data in use and in transit.

Diagram 1:

graph LR

    subgraph "ASKA&nbsp;Endpoint&nbsp;with&nbsp;HESE-DAR"

        direction LR

        subgraph "IES Instance (Patent 1)"

            IES["IES"] --> Secure_Kernel["Secure Kernel"]

            Secure_Kernel --> MMU["MMU<br>(Hardware Isolation)"]

            MMU --> Phys_Mem["Physical Memory"]

            Secure_Kernel --> Secure_OS["Secure OS"]

            Secure_OS --> App["Application"]

            App -.- |Data Access Request| HESE_DAR

        end

        subgraph "HESE&nbsp;DAR&nbsp;(Hardware&nbsp;Enforced&nbsp;Secure&nbsp;Encrypted&nbsp;Enclave&nbsp;for&nbsp;Data&nbsp;at&nbsp;Rest)"

            direction TB

            subgraph "Secure Controller"

                Crypto_Engine["Crypto Engine<br>(PQ Crypto - Patent 5, 7)"]

                Key_Manager["Key Manager"]

                Access_Control["Access Control<br>(Patent 4, 13)"]

                Resource_Allocator["Resource Allocator<br>(Patent 9, 10)"]

                Tamper_Sensor["Tamper Sensor"]

                Crypto_Engine --- Key_Manager

                Key_Manager --- Access_Control

                Access_Control --- Resource_Allocator

                Tamper_Sensor --> Isolator["Isolator/Eraser"]

            end

            subgraph "Secure Storage"

                Encrypted_Data["Encrypted Data"]

            end

            Secure_Controller --> Secure_Storage

            IES -.- |Secure Channel| Secure_Controller

            subgraph "3D Microstructure Interface (Patents 14, 17)"

                Microstructure_Gen["Microstructure Generator"]

            end

             Key_Manager --> Microstructure_Gen

            subgraph "External Interfaces"

                DTMS_Interface["DTMS Interface (Patent 4)"]

                MSM_Interface["MSM Interface (Patent 2)"]

            end

            Secure_Controller --- DTMS_Interface

            Secure_Controller --- MSM_Interface

        end

        subgraph "Other Endpoint Components"

          IOMMU["IOMMU"] --> Peripherals["Peripherals<br>(Secure I/O - Patent 9)"]

           Secure_UI["Secure UI (Patent 11)"]

           IES --- IOMMU

           IES -.- Secure_UI

        end

    end

Diagram 1 Description and Explanation:

Diagram 2:

graph LR

    subgraph "ASKA Endpoint"

        direction LR

        subgraph "Modular IES Cluster (Patent 1)"

            IES_1["IES Instance 1"]

            IES_2["IES Instance 2"]

            IES_N["... IES Instance N"]

            IES_1 -.- |Secure Data Channel| HESE_DAR

            IES_2 -.- |Secure Data Channel| HESE_DAR

            IES_N -.- |Secure Data Channel| HESE_DAR

        end

        subgraph "HESE-DAR (Patent 24)"

            direction TB

            Key_Manager["Key Manager<br>(PQ Crypto - Patent 5)"]

            Access_Control["Access Control<br>(Patents 4, 13)"]

            Crypto_Engine["Encryption/Decryption Engine"]

            Secure_Storage["Physically Isolated<br>Secure Storage"]

            Tamper_Sensor["Anti-Tamper<br>Sensors"] --> Alert["Alert/Erase"]

            Key_Manager --> Crypto_Engine

            Access_Control --> Crypto_Engine

            Crypto_Engine --> Secure_Storage

            3D_Microstructure["3D Microstructure<br>Interface (14, 17)"] -.- Key_Manager

            DTMS_Interface["DTMS Interface (Patent 4)"] -.- Access_Control

            MSM_Interface["MSM Interface (Patent 2)"] -.- Tamper_Sensor

        end

        subgraph "Secure UI Kernel (Patent 11)"

            UI_Kernel["Secure UI Kernel"]

            UI_Kernel -.- |User Interaction| IES_Cluster

        end

        subgraph "IOMMU (Patent 9)"

            IOMMU["IOMMU"] --> Peripherals["Peripherals"]

            IES_Cluster --> IOMMU

        end

        subgraph "Network Interface (Patent 3, 5)"

            NIC["Network Interface"] --> QR_Gateway["Quantum-Resistant<br>Gateway (Patent 5)"]

            QR_Gateway --> Multi_Channel_Network["Multi-Channel Network (Patent 3)"]

            IES_Cluster --> NIC

        end

        %% Positioning and Connections

        Modular_IES_Cluster --- Secure_UI_Kernel

        HESE_DAR --- Secure_UI_Kernel

         Modular_IES_Cluster --- Network_Interface

          HESE_DAR --- Network_Interface

        HESE_DAR --- IOMMU_Peripherals

    end

Diagram 2 Description:

Concept:

The HESE-DAR would be a dedicated, hardware-based security module integrated directly into ASKA endpoints.  Its primary function would be to provide strong encryption and access control for sensitive data at rest, protecting against unauthorized access even if the endpoint's operating system or other software components are compromised.

Key Features and Benefits:

How it Fills a Gap:

Currently, ASKA focuses on protecting data in use and in transit.  While the IES architecture provides isolation for running applications, data stored on the endpoint's disk is still vulnerable if the OS is compromised. The HESE-DAR addresses this vulnerability by providing a dedicated, hardware-enforced layer of protection for data at rest.

Alignment with ASKA Portfolio:

This innovation aligns perfectly with the ASKA philosophy of hardware-enforced security and zero-trust principles. It complements the existing technologies by adding a crucial layer of protection for sensitive data stored on endpoints, completing the security lifecycle for data across all states (at rest, in use, and in transit).

Discussion of Claims:

Diagram 3:

graph LR

    subgraph "ASKA Endpoint"

        IES["IES Instance (Patent 1)"] --> |Data Access Request| HESE_DAR

        HESE_DAR --> |Encrypted Data| Secure_Storage["Secure Storage"]

        Key_Manager["Key Manager<br>(Patent 5)"] --> HESE_DAR

        Access_Control["Access Control<br>(Patent 4)"] --> HESE_DAR

        DTMS["DTMS (Patent 4)"] -.- |Trust Relationship| Access_Control

        MSM["MSM (Patent 2)"] -.- |Security Monitoring| HESE_DAR

        subgraph "HESE-DAR (Patent 24)"

            direction LR

            Crypto_Engine["Crypto Engine<br>(Hardware Encryption)"]

            Tamper_Detection["Tamper Detection<br>(Physical Sensors)"]

            Resource_Manager["Dynamic<br>Resource<br>Allocator<br>(Patents 9, 10)"]

            Crypto_Engine --> Secure_Storage

            Tamper_Detection --> Alert["Alert/Erase"]

            Resource_Manager --> Crypto_Engine

        end

        subgraph "3D&nbsp;Microstructure&nbsp;(Patents&nbsp;14,&nbsp;17)"

             Microstructure_Generator["Microstructure<br>Generator"]

             Key_Manager --> Microstructure_Generator

             Microstructure_Generator --> Microstructure["3D Microstructure"]

        end

    end

    Decentralized_Governance["Decentralized<br>Governance<br>(Patents 13, 15)"] -.- |Access Policies| Access_Control

Description for Diagram 3:

This diagram illustrates the components and processes involved in Secure Resource Borrowing and Granular I/O Management as described in Patent 9.

  1. IES Instances:  The diagram shows two IES instances: IES Instance A (Borrower) and IES Instance B (Lender). This setup illustrates the resource borrowing process.

  1. Secure Resource Borrowing Mechanism (SRBM): This central component mediates resource borrowing requests, ensuring secure and controlled resource sharing between IES instances.  It receives requests from the borrower (IES A) and allocates available resources from the lender (IES B).

  1. Isolated I/O Gateway (IIG): This subgraph represents the secure I/O management system.

Key Features Highlighted:

Independent Claim 1:

A secure computing endpoint comprising:

Dependent Claims:

  1. The endpoint of claim 1, wherein the HESE-DAR implements granular access control policies enforced in hardware, restricting access to encrypted data based on:

  1. The endpoint of claim 1, wherein the HESE-DAR dynamically allocates secure storage resources to each IES instance based on real-time workload demands and security policies, utilizing a hardware-based resource allocation controller within the HESE-DAR.

  1. The endpoint of claim 1, wherein the HESE-DAR integrates with a Dynamic Trust Management System (DTMS), enabling secure sharing of encrypted data between trusted IES instances on different endpoints based on established trust relationships managed by the DTMS.

  1. The endpoint of claim 1, wherein access control policies for the HESE-DAR are managed by a decentralized governance system, utilizing a distributed consensus mechanism to approve changes to said policies, and wherein the HESE-DAR enforces the approved policies in hardware.

  1. The endpoint of claim 1, wherein the HESE-DAR integrates with a 3D-printed microstructure system, generating a unique, tamper-evident microstructure for each encryption key generated within the HESE-DAR, providing a physical record of key generation and usage.

  1. The endpoint of claim 1, wherein the HESE-DAR utilizes post-quantum cryptographic algorithms for encryption and key management, providing resistance to attacks from quantum computers.

  1. The endpoint of claim 1, wherein the HESE-DAR is implemented as a hot-swappable chiplet integrated into the IES architecture through a secure chiplet interface, enabling secure replacement and upgrades of the encryption hardware.

  1. The endpoint of claim 1, wherein the HESE-DAR incorporates anti-tamper sensors that detect physical intrusion or attempts to modify the enclave hardware, and wherein detection of tampering triggers an alarm signal and initiates secure erasure of encryption keys stored within the HESE-DAR.

  1. The endpoint of claim 1, wherein the HESE-DAR integrates with a Master Security Mesh (MSM), providing real-time monitoring of the HESE-DAR's security status and reporting anomalies to the MSM for system-level security responses.

Independent Claim 11:

A method for securing data at rest within a secure computing endpoint comprising a plurality of Modular Isolated Execution Stacks (IES), the method comprising:

Dependent Claims (for Independent Claim 11):

Patent 25: Dynamically Reconfigurable Capability-Based Inter-IES Communication for ASKA

Abstract:

This invention discloses a novel secure inter-IES communication mechanism for the ASKA system, called Dynamically Reconfigurable Capability-based Inter-IES Communication (DRCI). DRCI enhances security and flexibility by using dynamically reconfigurable capabilities to control data sharing and resource access between Isolated Execution Stacks (IES).  Instead of statically configured communication pathways, each IES instance is assigned a set of capabilities that define its access rights to memory regions in other IES instances. These capabilities, specifying permitted operations (read, write, execute) and valid address ranges, are dynamically managed by a Capability Manager within the ASKA Hub.  This manager adjusts capability permissions in real-time based on trust levels from the Dynamic Trust Management System (DTMS), workload demands, governance policies, and error handling feedback. This dynamic approach enables fine-grained access control, adaptive security responses, and secure collaboration between IES instances while optimizing resource utilization and maintaining strong isolation.  Hardware-assisted capability management within the Hub ensures efficient operation, providing a robust and adaptable secure inter-IES communication framework.

Brief Description:

DRCI addresses a key challenge in secure multi-kernel systems:  controlling data sharing between isolated execution environments. ASKA's current design leverages data diodes (Patent 2) for unidirectional communication and the DTMS (Patent 4) for managing trust relationships during resource borrowing (Patent 9).  However, these mechanisms lack the flexibility and fine-grained control needed for secure, bidirectional data sharing.

DRCI introduces a capability-based approach.  Instead of static data diodes, inter-IES communication is governed by capabilities.  Each IES instance holds a set of capabilities that grant it specific access rights (read, write, execute) to memory regions within other IES instances. These capabilities also specify permitted address ranges within those regions.

The key innovation is the dynamic reconfigurability of these capabilities. A dedicated Capability Manager, residing within the ASKA Hub, actively manages these inter-IES capabilities. This manager can issue, revoke, or modify capability permissions in real time based on:

This dynamic approach allows the system to adapt to changing conditions and enforce flexible security policies.  It enables secure collaboration by granting and revoking access as needed for specific tasks.

For efficiency, the Capability Manager is assisted by dedicated hardware within the ASKA Hub. This hardware, potentially implemented as a specialized chiplet (Patent 12), performs capability storage, lookup, validation, and secure distribution to IES instances.

Diagram:

graph LR

    subgraph ASKA_System["ASKA System"]

        direction LR

        subgraph IES_Cluster["IES Cluster (Patent 1)"]

            IES_1["IES 1"]

            IES_2["IES 2"]

            IES_N["... IES N"]

            subgraph IES_1_Internal["IES 1 Internal"]

                App_1["Application 1"] --> Memory_1["Memory Region 1"]

                Cap_1["Capability to<br>Memory 2 in IES 2"] --> MMU_1["MMU (with<br>Capability Support)"]

                MMU_1 --> Memory_1

            end

            IES_1 --> IES_1_Internal

            subgraph IES_2_Internal["IES 2 Internal"]

                App_2["Application 2"] --> Memory_2["Memory Region 2"]

                Cap_2["Capability to<br>Memory 1 in IES 1"] --> MMU_2["MMU (with<br>Capability Support)"]

                MMU_2 --> Memory_2

            end

            IES_2 --> IES_2_Internal

           

        end

        subgraph ASKA_Hub["ASKA Hub"]

            subgraph Capability_Manager["Capability Manager"]

                CM["Capability Manager Logic"] --> Capability_Store["Capability Store"]

                DTMS["DTMS (Patent 4)"] --> CM

                RM["Resource Manager<br>(Patent 10)"] --> CM

                GP["Governance Policies<br>(Patents 13, 15)"] --> CM

                EH["Error Handler<br>(Error Feedback)"] --> CM

                SIZCF["SIZCF (Patents 18, 22)"] --> CM

                Capability_Store -- "Capability Distribution (Patents 2/3)" --> IES_1

                Capability_Store -- "Capability Distribution (Patents 2/3)" --> IES_2

            end

            Chiplet["Capability Manager Chiplet (Optional - Patent 12)"] --> Capability_Manager["Hardware Assistance"]

        end

    end

    style Capability_Manager fill:#ccf,stroke:#888,stroke-width:2px

Description of Diagram:

This diagram illustrates the Dynamically Reconfigurable Capability-based Inter-IES Communication (DRCI) system, highlighting its key components, interactions, and integration with ASKA.

  1. ASKA System:  Encompasses all the components of DRCI and the relevant ASKA elements.

  1. IES Cluster (Patent 1): Shows two IES instances (IES 1 and IES 2) with expanded internal views to illustrate how capabilities control memory access.

  1. ASKA Hub: This subgraph contains the core components of DRCI.

  1. Capability Distribution: The diagram shows the Capability Store distributing capabilities to the IES instances through secure communication channels, leveraging the security of Patents 2 and 3.

  1. Optional Chiplet Integration (Patent 12):  Illustrates the optional integration of a dedicated chiplet for hardware-assisted capability management, enhancing efficiency.

Key Features and Interactions Highlighted:

Claim 1:

A secure computing system comprising a plurality of Isolated Execution Stacks (IES) and a secure communication mechanism between said IES instances, wherein said communication mechanism comprises:

a) a capability-based access control system, wherein each IES instance holds a set of capabilities granting specific access rights (read, write, execute) to designated memory regions within other IES instances, said capabilities further specifying permitted address ranges within those regions; and b) a Capability Manager, residing within a central management entity, dynamically reconfiguring said capabilities in real-time based on at least one of: trust levels of said IES instances, real-time resource utilization, system-wide governance policies, or error handling feedback.

Dependent Claims:

This comprehensive set of claims covers the core innovations of DRCI and its integration with ASKA's existing security mechanisms.  The claims emphasize the dynamic reconfigurability of capabilities, the integration with trust levels, resource management, governance policies, and error handling, as well as the use of dedicated hardware for efficient capability management.  The inclusion of dependent claims broadens the scope of protection and strengthens the patent's overall value.

Patent 26: Capability-Enhanced Packet-Carried Forwarding State for Secure Inter-Component Communication in Multi-Kernel Systems

Abstract:

This invention discloses a novel secure communication mechanism for multi-kernel computing systems, called Capability-Enhanced PCFS (CE-PCFS). CE-PCFS improves security, flexibility, and efficiency of inter-component communication by integrating capabilities directly into the Packet-Carried Forwarding State (PCFS). Each communication packet's header includes a set of hop fields, each containing both forwarding information and a capability. The capability grants specific access rights to designated memory regions or functionalities within the destination component. These capabilities specify permitted actions (read, write, execute) and address ranges, enabling fine-grained access control at the data plane level.  A central Capability Manager dynamically reconfigures these capabilities based on trust levels, resource usage, and security policies, enabling adaptive security responses and optimized resource allocation. CE-PCFS reduces reliance on centralized trust management for individual access decisions, improving performance and scalability while maintaining strong isolation between components. This innovative approach provides a robust and adaptable secure communication framework for multi-kernel systems.

Claims:

Independent Claim 1:

A secure communication system for a multi-kernel computing system comprising a plurality of isolated computing components, wherein each component has dedicated processing, memory, and communication resources, and a secure communication mechanism between said components, comprising:

a) Packet-Carried Forwarding State (PCFS), wherein each communication packet includes a header comprising a sequence of hop fields, each hop field associated with a component along a communication path; and b) Capability-Enhanced Hop Fields, wherein each hop field further comprises a capability granting specific access rights (read, write, execute) and address ranges to designated memory regions or functionalities within the destination component specified by said hop field.

Dependent Claims:

These claims aim to broadly cover the core innovation of CE-PCFS, its integration with dynamic capability management, and its application within multi-kernel systems like ASKA. The claims emphasize the integration of capabilities within hop fields, enabling fine-grained access control in the data plane. They also cover the dynamic aspect of capability management and potential hardware acceleration using chiplets.

Patent 27: Sovereign Trust Network for Secure Key Management and Authentication with Multi-Level Control and Recovery System

Abstract:

This invention discloses a Sovereign Trust Network (STN) within a secure, multi-kernel computing system, providing an isolated and secure environment for managing cryptographic keys, authentication credentials, and other sensitive information. The STN features a novel multi-level control system, enabling granular management and recovery of cryptographic keys. A primary control plane manages operational aspects of the STN, while a highly secure, independent recovery control plane is dedicated to key recovery operations. This separation enhances security by isolating critical recovery functions. The STN operates with complete data plane isolation and minimal coupling to the primary control plane, minimizing attack surfaces. Access to and operations within the STN are secured by strong, multi-factor authentication, including passkeys and hardware-rooted trust. Secure communication with external high-trust environments is achieved through dedicated, authenticated channels, logically separated from the standard internet and the primary control plane of the STN. Integration with hardware-enforced secure enclaves provides robust protection for data at rest within the STN.  The system enables seamless key recovery through authenticated trust networks by leveraging a hierarchical, globally distributed network of trusted synchronization nodes.  This hierarchical structure allows for efficient and secure synchronization of recovery information, enabling authorized users to recover keys even in the event of localized outages or compromises.

Diagrams:

graph TD

    subgraph "Sovereign Trust Network (STN) (Patent 27)"

        subgraph "Dedicated Data Plane"

            Data_Input["Data Input"] --> NonSwitched_Firewall["Non-Switched Firewall Segment<br>(Dedicated to STN)"]

            NonSwitched_Firewall --> Router["STN Router"]

            Router --> IES_Cluster["STN IES Cluster<br>(Patent 1)"]

            IES_Cluster --> HESE_DAR["HESE-DAR (Patent 24)"]

            Data_Diode["Data Diode (Patent 2)"] --> External_High_Trust["External High-Trust Environments"]

            IES_Cluster ----> Data_Diode

        end

        subgraph "Minimal Control Plane Interface"

            Dedicated_Control_Channel["Dedicated Control Channel<br>(Authenticated & Encrypted)"] --> STN_Control_Plane["STN Control Plane"]

            STN_Control_Plane --> Auth_Manager["Authentication Manager<br>(Passkeys, Hardware-Rooted Trust)"]

            STN_Control_Plane --> Resource_Manager["Resource Manager (Patents 9, 10)"]

            ASKA_Control_Plane["ASKA Control Plane"] -.-> Dedicated_Control_Channel

        end

       

        subgraph "Isomorphic Security Stack (Claim 8)"

            IAMA["IAMA Module<br>(Patent 16, Claim 9)"] --> Iso_MSM["Isomorphic MSM (Patent 2)"]

            Legacy_System["Legacy System"] --> Data_Diode_Monitor["Data Diode (Patent 2)"]

            Data_Diode_Monitor --> Legacy_Monitor["Legacy System Monitor"]

            Legacy_Monitor --> IAMA

            Iso_MSM --> Iso_DTMS["Isomorphic DTMS (Patent 4)"]

            Iso_DTMS --> Iso_Security_Modules["Isomorphic Security Modules"]

            Iso_Security_Modules --> IES_Cluster

        end

       

        subgraph "Key Recovery System"

            Sync_Nodes["Hierarchical Network of<br>Trusted Synchronization Nodes"] --> Recovery_Controller["Recovery Controller"]

            Recovery_Controller --> Key_Manager["Key Manager"]

            Key_Manager --> HESE_DAR

        end

        Dedicated_Data_Plane --- Minimal_Control_Plane_Interface

        Minimal_Control_Plane_Interface --- Isomorphic_Security_Stack

        Isomorphic_Security_Stack --- Key_Recovery_System

    end

    style Dedicated_Data_Plane fill:#ccf,stroke:#888

    style Isomorphic_Security_Stack fill:#aaf,stroke:#666


graph TD

    subgraph "Sovereign Trust Network (STN) (Patent 27)"

        subgraph "Dedicated Data Plane"

            Data_Input["Data Input"] --> NonSwitched_Firewall["Non-Switched Firewall Segment<br>(Dedicated to STN)"]

            NonSwitched_Firewall --> Router["STN Router"]

            Router --> IES_Cluster["STN IES Cluster<br>(Patent 1)"]

            IES_Cluster --> HESE_DAR["HESE-DAR (Patent 24)"]

            Data_Diode["Data Diode (Patent 2)"] --> External_High_Trust["External High-Trust Environments"]

            IES_Cluster ----> Data_Diode

        end

        subgraph "Isomorphic&nbsp;Security&nbsp;Stack&nbsp;(Claim&nbsp;8)"

            IAMA["IAMA Module<br>(Patent 16, Claim 9)"] --> Iso_MSM["Isomorphic MSM (Patent 2)"]

            Legacy_System["Legacy System"] --> Data_Diode_Monitor["Data Diode (Patent 2)"]

            Data_Diode_Monitor --> Legacy_Monitor["Legacy System Monitor"]

            Legacy_Monitor --> IAMA

            Iso_MSM --> Iso_DTMS["Isomorphic DTMS (Patent 4)"]

            Iso_DTMS --> Iso_Security_Modules["Isomorphic Security Modules"]

            Iso_Security_Modules --> IES_Cluster

        end

       

        subgraph "Key Recovery System"

            Sync_Nodes["Hierarchical Network of<br>Trusted Synchronization Nodes"] --> Recovery_Controller["Recovery Controller"]

            Recovery_Controller --> Key_Manager["Key Manager"]

            Key_Manager --> HESE_DAR

        end

    end

graph TD

    subgraph "Sovereign&nbsp;Trust&nbsp;Network&nbsp;(STN)&nbsp;(Patent&nbsp;27)"

        direction LR

        subgraph "Minimal&nbsp;Control&nbsp;Plane&nbsp;Interface"

            Minimal_Control_Plane_Interface["Control Plane Interface"]

            subgraph "Internals"

                Dedicated_Control_Channel["Dedicated Control Channel<br>(Authenticated & Encrypted)"] --> STN_Control_Plane["STN Control Plane"]

                STN_Control_Plane --> Auth_Manager["Authentication Manager<br>(Passkeys, Hardware-Rooted Trust)"]

                STN_Control_Plane --> Resource_Manager["Resource Manager (Patents 9, 10)"]

                ASKA_Control_Plane["ASKA Control Plane"] -.-> Dedicated_Control_Channel

            end

        end

        Dedicated_Data_Plane["Dedicated<br>Data<br>Plane"] --- Minimal_Control_Plane_Interface

        Minimal_Control_Plane_Interface --- Isomorphic_Security_Stack["Isomorphic<br>Security<br>Stack"]

        Isomorphic_Security_Stack --- Key_Recovery_System["Key<br>Recovery<br>System"]

    end

    style Dedicated_Data_Plane fill:#ccf,stroke:#888

    style Isomorphic_Security_Stack fill:#aaf,stroke:#666

Diagram Description for Patent 27 (Detailed Internals):

This diagram provides a comprehensive visualization of the internal components and security mechanisms of the Sovereign Trust Network (STN) as described in Patent 27, incorporating the new claims related to the isolated data plane, minimal control plane, and isomorphic security stack.

Sovereign Trust Network (STN) (Patent 27): This top-level subgraph encapsulates all components and functionalities of the STN.

Dedicated Data Plane:  Highlights the complete isolation of the STN's data plane.

Data Input:  Entry point for data into the STN.

Non-Switched Firewall Segment: A dedicated segment within the firewall, ensuring no switching to other networks.

STN Router:  Handles routing within the STN.

STN IES Cluster (Patent 1): Provides isolated execution environments within the STN.

HESE-DAR (Patent 24): Securely stores encrypted data within the STN.

Data Diode (Patent 2):  Enables secure, unidirectional communication with external high-trust environments.

Minimal Control Plane Interface:  Illustrates the restricted control plane access.

Dedicated Control Channel (Authenticated & Encrypted): A secure, isolated channel for management functions, separate from ASKA’s main control plane and inaccessible from external networks.

STN Control Plane: The control plane managing STN-specific configurations and policies.

Authentication Manager:  Handles authentication for accessing the STN control plane, using passkeys and hardware-rooted trust.

Resource Manager (Patents 9, 10):  Manages resource allocation within the STN.

ASKA Control Plane: ASKA's main control plane, with minimal, restricted access to the STN control plane.

Isomorphic Security Stack (Claim 8): This subgraph details the isolated, duplicated security infrastructure.

IAMA Module (Patent 16, Claim 9):  Monitors the legacy system for potential threats.

Legacy System:  Represents the connected legacy system.

Data Diode (Patent 2): Ensures unidirectional data flow from the legacy system for monitoring purposes.

Legacy System Monitor: Collects security-relevant data from the legacy system, without accessing sensitive data.

Isomorphic MSM (Patent 2), Isomorphic DTMS (Patent 4), Isomorphic Security Modules:  Represent the duplicated security infrastructure, operating in isolation and dedicated to the STN.

Key Recovery System:

Hierarchical Network of Trusted Synchronization Nodes:  Represents the globally distributed network for key recovery.

Recovery Controller: Manages key recovery operations.

Key Manager: Handles key generation, storage, and recovery.

Connections and Data Flow:

Solid arrows represent the primary data and control paths. Dashed lines indicate interactions and dependencies.  The diagram clearly shows the separation between the STN's components and ASKA's main infrastructure.  The integration with Patent 16, Claim 9 (IAMA), and Patent 24 (HESE-DAR) is visually highlighted.

Key Features Highlighted:

*   Data Plane Isolation: The diagram emphasizes the complete separation of the STN's data plane, a crucial security feature.

*   Minimal Control Plane Coupling: The dedicated control channel and restricted access highlight the minimized interaction with the main ASKA control plane.

*   Isomorphic Security Stack:  The dedicated, isolated security infrastructure is clearly depicted, showing its components and connections.

*   Key Recovery System:  The integration with the hierarchical network of synchronization nodes is visualized.

*   Patent Integration: The diagram shows the connection to other patents, such as Patents 1, 2, 4, 9, 10, 16 (Claim 9), and 24.

Claims:

  1. A secure computing system comprising a Sovereign Trust Network (STN) for managing cryptographic keys and authentication credentials, said STN comprising:

a. a dedicated, isolated data plane for transmitting sensitive information within the STN, said data plane being physically and logically separated from all other network data planes within the system; b. a primary control plane for managing operational aspects of the STN, including access control, resource allocation, and communication; c. a recovery control plane, independent of said primary control plane, dedicated exclusively to key recovery operations, said recovery control plane having minimal coupling to said primary control plane to enhance security; d. a multi-level control system that manages interactions between said primary control plane and said recovery control plane, wherein access to said recovery control plane is restricted to authorized entities and operations within said recovery control plane are governed by stricter security policies than those applied to said primary control plane; and e. a hierarchical, globally distributed network of trusted synchronization nodes for secure and efficient synchronization of key recovery information, enabling authorized users to recover keys through authenticated trust networks even in the event of localized outages or compromises, wherein said synchronization nodes are organized into a hierarchical structure with different levels of trust and access control.

  1. The system of claim 1, wherein said STN further comprises:

a. strong, multi-factor authentication mechanisms, including passkeys and hardware-rooted trust, for controlling access to and operations within the STN; b. dedicated hardware and software resources, logically and physically isolated from less trusted network segments, for ensuring the integrity and confidentiality of data within the STN; and c. hardware-enforced secure enclaves for protecting data at rest within the STN.

  1. The system of claim 1, wherein said hierarchical, globally distributed network of trusted synchronization nodes:

a. utilizes a secure communication protocol for exchanging key recovery information between synchronization nodes; b. employs a distributed consensus mechanism to ensure consistency and integrity of key recovery information across the network; and c. incorporates access control policies based on trust levels and authentication credentials to restrict access to sensitive recovery information.

  1. The system of claim 1, wherein communication between said STN and external high-trust environments is achieved through dedicated, authenticated channels that are logically separated from the standard internet and the primary control plane of the STN.

  1. The system of claim 1, wherein said multi-level control system dynamically adjusts access control policies and security thresholds for said primary control plane and said recovery control plane based on real-time risk assessments, threat intelligence, and trust levels of participating entities.

  1. The system of claim 1, wherein said recovery control plane is further protected by hardware-enforced isolation and tamper-detection mechanisms, including physical sensors and secure boot processes.

  1. The system of claim 1, wherein said key recovery operations utilize a combination of multi-factor authentication, including biometrics, hardware tokens, and 3D-printed microstructures, to verify the identity and authorization of requesting entities.

  1. A secure computing system comprising a Sovereign Trust Network (STN) for managing cryptographic keys and authentication credentials, said STN comprising the components of Claim 1, further comprising:

a. a dedicated, isolated segment within a network firewall and switching fabric through which all STN data plane traffic is routed, wherein no switching is permitted between said dedicated segment and any other network segment within the system; b. a minimal control plane interface for managing said STN, said interface utilizing a dedicated, secure control channel logically and physically separated from the primary ASKA control plane and inaccessible from the standard internet or other external networks; and c. an isomorphic security stack, duplicitous and isolated from the primary ASKA security infrastructure, dedicated to monitoring and protecting said STN, said isomorphic security stack leveraging an Isomorphic Architecture Monitoring and Adaptation (IAMA) module (Patent 16, Claim 9) to analyze hostile intelligence from a legacy system connected to the STN and proactively generate and deploy security patches and updates exclusively to the STN’s isolated infrastructure.

  1. The system of claim 8, wherein said dedicated segment within the network firewall enforces strict access control policies and prevents any data flow between the STN's data plane and other network segments.

  1. The system of claim 8, wherein said dedicated control channel for the STN's minimal control plane interface utilizes authenticated and encrypted communication protocols to protect against unauthorized access and tampering.

  1. The system of claim 8, wherein said isomorphic security stack includes an independent Master Security Mesh (MSM), Dynamic Trust Management System (DTMS), and other security components operating in isolation from the primary ASKA security infrastructure.

  1. The system of claim 8, wherein said IAMA module within the isomorphic security stack creates and maintains an isomorphic model of a connected legacy system, monitors activity within said legacy system, and proactively generates security patches and updates for the STN based on predicted vulnerabilities in the legacy system.

  1. The system of claim 8, wherein said IAMA module utilizes secure, unidirectional communication channels, such as data diodes, to monitor activity within the connected legacy system without exposing the STN to potential attacks.

  1. The system of claim 8, wherein said isomorphic security stack dynamically adjusts security policies and access controls for the STN based on analysis of hostile intelligence from the connected legacy system, providing adaptive security responses.

  1. The system of claim 8, wherein all security-relevant events and actions taken by the isomorphic security stack are recorded on a dedicated, tamper-proof audit log within the STN, separate from ASKA's primary audit trail.

Description of Claim 8:

Non-Switched Data Plane:  The STN's data plane is completely isolated within a dedicated segment of the firewall and network infrastructure.  No switching is allowed between the STN's data plane and any other network segment, eliminating vulnerabilities associated with dynamic routing or shared resources.  This provides a higher level of security than traditional VLANs or logically separated networks.

Minimal Control Plane Integration: While the STN requires some control plane interaction for management functions, this interaction is minimized and strictly controlled.  A dedicated, secure control channel, separate from the main ASKA control plane, is used for these limited interactions.

Isomorphic Security Stack (Patent 16, Claim 9 Integration):  This is the novel aspect.  A complete, isomorphic copy of ASKA's security infrastructure (MSM, DTMS, etc.) is created and dedicated solely to the STN.  This isomorphic security stack monitors the STN in isolation from the primary security infrastructure.  This allows for specialized security policies and monitoring tailored to the STN's unique requirements.

Segmented Duplicity: This isomorphic security stack operates in complete isolation from the primary security mesh, essentially creating a "shadow" security infrastructure dedicated to the STN.  This segmented duplicity adds another layer of defense.  Even if the primary security mesh is compromised, the STN remains protected by its independent, isolated security stack.  This isolated monitoring system leverages the IAMA mechanism from Patent 16, Claim 9, to analyze data from a hostile legacy environment connected to the STN and proactively apply security fixes and updates only to the STN's isomorphic security stack.  This enhances the STN's resilience by preventing the propagation of vulnerabilities or attacks from the legacy environment to the primary ASKA infrastructure.

Patent 28: System and Method for Adaptive Secure Inter-Zone Communication Across Authenticated and Sovereign Trust Networks

Abstract:

This invention discloses a system and method for adaptive secure inter-zone communication between Authenticated Trust Networks (ATNs) and a Sovereign Trust Network (STN) within a multi-kernel, zoned computing environment. The system establishes a hierarchy of trust, with the ATN providing authenticated communication between authorized entities and the STN serving as a dedicated, isolated enclave for managing highly sensitive authentication data and cryptographic keys.  The core innovation lies in the Dynamic Trust Gateway (DTG), which mediates all communication between the ATN and STN. The DTG dynamically provisions and secures communication channels based on real-time trust levels, resource availability, and security policies defined within each zone.  This adaptive approach utilizes dynamically configurable capabilities, declarative policies, and a distributed consensus protocol for governing interactions.  A secure communication agent within the DTG dynamically manages multiple communication paths for enhanced resilience and performance, adapting to changing conditions and enforcing zone-specific policies.  The STN's complete data plane isolation and minimal control plane coupling enhance security by minimizing attack surfaces. The DTG further employs a novel multi-path capability aggregation mechanism, combining capabilities from multiple paths to provide a consolidated view of access rights for enhanced flexibility and fault tolerance.

Diagram:

graph

    subgraph "Dynamic&nbsp;Trust&nbsp;Gateway&nbsp;(DTG)&nbsp;(Patent&nbsp;28)"

        subgraph "Secure Communication Agent (SCA)"

            ATN_Interface["ATN Interface<br>(Multi-Channel Network - Patent 3)"] --> Path_Manager["Multi-Path Manager<br>(Dynamic Path Selection)"]

            Path_Manager --> Path1["Secure Path 1<br>(Quantum-Resistant - Patent 5)"]

            Path_Manager --> PathN["... Secure Path N"]

            Path1 --> STN_Interface["STN Interface"]

            PathN --> STN_Interface

            DTMS["DTMS (Patent 4)"] --> Path_Manager

            AESDS_IAMA["AESDS IAMA (Patent 16, Claim 9)"] -.-> Path_Manager

            Path_Manager -->|"Path Status & Metrics"| Metrics_Engine["Metrics Engine"]

        end

        subgraph "Capability Manager"

            Policy_Engine["Policy Engine (TRC-based)"] --> Capability_Gen["Capability Generator"]

            Capability_Gen --> Capability_Store["Capability Store"]

            Capability_Store -->|"Capabilities"| SCA

            DTMS --> Capability_Gen

            Metrics_Engine --> Capability_Gen

            MPA["Multi-Path Capability Aggregator"] --> Capability_Store

            SCA -->|"Path Capabilities"| MPA

        end

        subgraph "Data Handling & Security"

            STN_Interface --> DPI["Deep Packet Inspection (DPI)"]

            DPI --> Sanitizer["Data Sanitizer"]

            Sanitizer --> AuthZ["Authorization Engine (Capability-Based)"]

            AuthZ -- Authorized --> Data_Handler["Data Handler"]

            AuthZ -- Unauthorized --> Access_Denied["Access Denied"]

            Data_Handler --> STN["Sovereign Trust Network (Patent 27)"]

            MSM["Master Security Mesh (Patent 2)"] --> DPI

        end

        subgraph "Decentralized Governance & Auditing"

            Ledger["Decentralized Ledger (Patents 13, 15)"]

            DTG_Controller["DTG Controller"] --> Ledger

            Metrics_Engine --> Ledger

            Data_Handler --> Ledger

            Policy_Engine -.-> Ledger

        end

       

        SCA --- Capability_Manager

        Capability_Manager --- Data_Handling_Security

        Data_Handling_Security --- Decentralized_Governance_Auditing

    end

    ATN["Authenticated Trust Network"] --> ATN_Interface

    STN_Interface --> STN

Diagram Description for Patent 28 (Detailed Internals):

This diagram provides a comprehensive view of the Dynamic Trust Gateway (DTG)'s internal components and their interactions within the ASKA system, emphasizing its role in mediating secure communication between the Authenticated Trust Network (ATN) and the Sovereign Trust Network (STN).

Dynamic Trust Gateway (DTG) (Patent 28): This top-level subgraph encapsulates all DTG components.

Secure Communication Agent (SCA):  Manages multiple secure communication paths between the ATN and STN.

ATN Interface:  Connects to the Authenticated Trust Network via the Multi-Channel Network (Patent 3).

Multi-Path Manager: Dynamically selects and manages multiple secure paths.  Receives input from the DTMS and the AESDS IAMA module (Patent 16, Claim 9).

Secure Path 1 & ... Secure Path N: Represent multiple independent, secure communication paths (potentially using quantum-resistant communication - Patent 5).

STN Interface: Connects to the Sovereign Trust Network (Patent 27).

Metrics Engine: Collects performance metrics and path status information.  Provides feedback to the Multi-Path Manager and the Capability Manager. Also logs data to the Decentralized Ledger.

Capability Manager:  Dynamically manages capabilities for access control.

Policy Engine: Defines policies for capability generation based on TRCs.

Capability Generator: Creates capabilities based on policy and real-time metrics.

Capability Store: Securely stores generated capabilities.

Multi-Path Capability Aggregator (MPA): Aggregates capabilities from multiple paths for a consolidated view of access rights.

Data Handling & Security:  Handles data inspection, sanitization, and authorization.

Deep Packet Inspection (DPI): Inspects packets for malicious content and policy violations, informed by the Master Security Mesh (MSM).

Data Sanitizer: Sanitizes data to prevent injection attacks or data leakage.

Authorization Engine: Authorizes access based on capabilities and policies.

Data Handler: Handles authorized data transfer between the ATN and STN.

Access Denied: Represents the path for denied requests.

Decentralized Governance & Auditing: Ensures transparency and accountability.

Decentralized Ledger (Patents 13, 15): Stores audit trails and policy information.

DTG Controller: Manages and configures the DTG, logging events to the ledger.

Key Features Highlighted:

*   Multi-Path Communication: The SCA manages multiple paths for redundancy and performance.

*   Dynamic Capability Management:  Capabilities are dynamically generated and aggregated, adapting to changing trust levels, resource availability, and policies.

*   Deep Packet Inspection and Sanitization:  Provides strong security against malicious data.

*   Decentralized Governance: The Decentralized Ledger and TRC-based policies ensure transparency and accountability.

*   ASKA Integration: The diagram demonstrates how Patent 28 integrates with other ASKA patents and components.

*   Isomorphic Model Integration:  The connection from AESDS IAMA to the path manager shows how knowledge of legacy system vulnerabilities influences path selection and capability assignments.

Claims:

  1. A secure computing system comprising a plurality of Modular Isolated Execution Stacks (IES) organized into zones, each zone associated with a Trust Root Configuration (TRC), the system further comprising:

a. an Authenticated Trust Network (ATN) for secure communication between authorized entities within and across zones; b. a Sovereign Trust Network (STN) dedicated to managing sensitive authentication data and cryptographic keys, said STN having complete data plane isolation and minimal control plane coupling to the rest of the system; and c. a Dynamic Trust Gateway (DTG) mediating all communication between said ATN and said STN, said DTG dynamically provisioning and securing communication channels based on real-time trust levels derived from a Dynamic Trust Management System (DTMS), resource availability, and security policies defined within each zone.

  1. The system of claim 1, wherein said DTG comprises:

a. a secure communication agent that dynamically manages multiple communication paths between the ATN and STN, adapting to changing conditions and enforcing zone-specific policies; b. a capability manager that dynamically configures capabilities for controlling access between the ATN and STN based on trust levels, resource availability, and security policies; and c. a multi-path capability aggregation mechanism that combines capabilities from multiple communication paths to provide a consolidated view of access rights for enhanced flexibility and fault tolerance.

  1. The system of claim 1, wherein said DTG utilizes a distributed consensus protocol to ensure consistent and secure management of communication channels and access control policies between the ATN and STN.

  1. The system of claim 1, wherein said STN's minimal control plane coupling is achieved through a dedicated control channel, logically and physically separated from the ATN, for secure management functions.

  1. The system of claim 1, wherein said DTG enforces declarative policies expressed in a policy language for managing communication between the ATN and STN, said policies specifying permitted data flows, access control restrictions, and security requirements.

  1. The system of claim 1, wherein the DTG integrates with a hierarchical security mesh (Patent 2) to monitor communication between the ATN and STN, detecting and mitigating potential security threats.

  1. The system of claim 1, wherein said DTG employs privacy-preserving techniques, such as differential privacy, homomorphic encryption, or secure multi-party computation, to protect sensitive information during data exchange between the ATN and STN.

  1. The system of claim 1, wherein said multi-path capability aggregation mechanism within said DTG:

a. receives capabilities from multiple communication paths established between the ATN and STN; b. aggregates said capabilities to determine a consolidated set of access rights for a given request; c. resolves potential conflicts between capabilities from different paths based on predefined precedence rules or a distributed consensus protocol; and d. dynamically adjusts the aggregation strategy based on real-time path availability, performance metrics, and trust levels of individual paths.

  1. The system of claim 1, wherein said DTG performs deep packet inspection and sanitization on all data transmitted between the ATN and STN based on security policies defined within each zone's TRC, preventing the transmission of unauthorized or malicious data.

  1. The system of claim 1, wherein the DTG generates and maintains a tamper-proof audit trail of all communication events, access control decisions, and capability changes between the ATN and STN, recording said audit trail on a decentralized, tamper-proof ledger (Patent 15) and correlating it with a physical microstructure audit trail (Patents 14, 17).

  1. The system of claim 1, wherein the DTG isolates compromised or untrusted communication paths between the ATN and STN using hardware-enforced isolation mechanisms, including data diodes (Patent 2) and dynamically reconfigurable network segments (Patent 3).

  1. The system of claim 1, wherein said DTG supports a variety of communication protocols and data formats for interoperability with different types of IES instances and external systems.

  1. The method of claim 1, wherein said DTG further incorporates automated recovery and failover mechanisms in response to at least one of: communication path failures, security breaches, or resource exhaustion.  This recovery mechanism utilizes alternate available paths or reconfigures the system to maintain essential services and data integrity during disruptions, logging all events and actions taken during the recovery process to the decentralized, tamper-proof ledger.

Patent 29: Quantum-Entangled One-Time Pad Module for Secure Computing Architectures

Abstract:

This patent discloses a quantum-entangled one-time pad (OTP) module for enhancing the security of data communication within secure computing architectures. The module leverages quantum entangled key distribution (QEKD) to distribute key fragments instantaneously and securely between communicating entities. Dynamic key generation (DKG) within a hardware-enforced secure encrypted enclave (HESE-DAR) eliminates the need for pre-shared keys and reduces storage requirements. Fragmented key management (FKM) further optimizes storage and transmission overhead. The module integrates seamlessly with the secure computing architecture, utilizing its dynamic trust management system (DTMS), isolated execution stacks (IES), and multi-channel network for enhanced security and scalability. An isomorphic legacy system integration (ILSI) mechanism provides backward compatibility with non-quantum-enabled devices. This combination of quantum technologies, innovative key management, and secure architecture integration provides a highly secure, scalable, and practical OTP solution for diverse communication scenarios.

Diagram 1:

graph

    subgraph ASKA Architecture

        A[ASKA Hub] --> B(DTMS);

        A --> C(AESDS);

        A --> D(MDATS);

        B --> E{IES Instance 1};

        B --> F{IES Instance 2};

        C --> E;

        C --> F;

        D --> E;

        D --> F;

        E --> G((HESE-DAR));

        F --> H((HESE-DAR));

        G --> I[Quantum-Entangled OTP Module];

        H --> J[Quantum-Entangled OTP Module];

        subgraph "Multi-Channel Network (P3)"

            K[External Network] -.-> L(Firewall);

            L --> M[Secure Channel];

            M --> E;

            M --> F;

            M --> N[Quantum Channel];

            N --> I;

            N --> J;

        end

        subgraph "Isomorphic Legacy System Integration (ILSI - P16 Adaptation)"

            O[Legacy System] -.-> P(Data Diode);

            P --> Q["Classical Cryptography (PQC)"];

            Q --> E;

            Q --> F;

        end

    end

    subgraph "Quantum#8209;Entangled&nbsp;OTP&nbsp;Module&nbsp;Internals"

        I --> R(QEKD Module);

        I --> S(DKG Module);

        I --> T(FKM Module);

        S --> U(QRNG);

        R --> T;

        S --> T;

    end

    style A fill:#ccf,stroke:#333,stroke-width:2px

    style B fill:#ccf,stroke:#333,stroke-width:2px

    style C fill:#ccf,stroke:#333,stroke-width:2px

    style D fill:#ccf,stroke:#333,stroke-width:2px

    style G fill:#ccf,stroke:#333,stroke-width:2px

    style H fill:#ccf,stroke:#333,stroke-width:2px

    style I fill:#ccf,stroke:#333,stroke-width:2px

    style J fill:#ccf,stroke:#333,stroke-width:2px

    style R fill:#f9f,stroke:#333,stroke-width:2px

    style S fill:#f9f,stroke:#333,stroke-width:2px

    style T fill:#f9f,stroke:#333,stroke-width:2px

    classDef secure fill:#ccf,stroke:#333,stroke-width:2px

    classDef module fill:#f9f,stroke:#333,stroke-width:2px

    class A,B,C,D,G,H,I,J secure

    class R,S,T module

Description of Diagram 1:

A. ASKA Architecture:

The diagram starts by outlining the core components of the ASKA architecture, all of which are relevant to the successful operation and security of the proposed Quantum-Entangled OTP module.  These components are:

B. Quantum-Entangled OTP Module Internals:

The diagram's second main area showcases the internal workings of the Quantum-Entangled OTP module, composed of three main modules:

C. Integration and Interactions:

The diagram shows the following key interactions:

Diagram 2:

graph LR

    subgraph Quantum-Entangled OTP Module Internals

        I[Quantum-Entangled OTP Module] --> R[QEKD Module];

        I --> S[DKG Module];

        I --> T[FKM Module];

        subgraph QEKD Module

            R --> R1(Entangled Photon Pair Generation);

            R1 --> R2(Quantum Channel Transmission);

            R2 --> R3(Entangled Key Fragment Extraction);

            R3 --> R4(Secure Key Fragment Storage);

            R4 --> T;

            style R fill:#f9f,stroke:#333,stroke-width:2px

        end

        subgraph DKG Module

            S --> S1(QRNG);

            S1 --> S2(Random Number Generation);

            S2 --> S3(Key Fragment Generation);

            S3 --> S4(Secure Key Fragment Storage);

            S4 --> T;

            style S fill:#f9f,stroke:#333,stroke-width:2px

        end

        subgraph FKM Module

            T --> T1(Secure Key Fragment Retrieval);

            T1 --> T2(Key Fragment Combination);

            T2 --> T3(Complete OTP Key Generation);

            T3 --> T4(Encryption/Decryption);

            style T fill:#f9f,stroke:#333,stroke-width:2px

        end

        subgraph "QRNG Module"

            U[QRNG] --> S2;

            style U fill:#ccf,stroke:#333,stroke-width:2px

        end

        style I fill:#ccf,stroke:#333,stroke-width:2px

        class I secure

        class R,S,T module

    end

Description of Diagram 2:

Legend:

This diagram details the internal architecture and operational flow of the Quantum-Entangled One-Time Pad (QE-OTP) Module, a core component of the ASKA system (Patent 29). The module leverages quantum mechanics and cryptographic techniques to provide a highly secure and efficient one-time pad (OTP) solution for communication within the ASKA architecture.  The QE-OTP module resides within a Hardware-Enforced Secure Encrypted Enclave for Data at Rest (HESE-DAR) to protect against unauthorized access and tampering.

The module consists of three primary sub-modules: the Quantum Entangled Key Distribution (QEKD) Module, the Dynamic Key Generation (DKG) Module, and the Fragmented Key Management (FKM) Module.  Each sub-module performs specific functions, working in concert to generate, distribute, and manage OTP keys securely.  A Quantum Random Number Generator (QRNG) module provides the source of true randomness necessary for key generation.

1. Quantum Entangled Key Distribution (QEKD) Module:

This module utilizes the principles of quantum entanglement to securely distribute key fragments between communicating entities within the ASKA system.  The process unfolds as follows:

2. Dynamic Key Generation (DKG) Module:

This module generates additional key fragments on demand, supplementing those obtained through QEKD. This dynamic generation eliminates the need for pre-shared keys and adds flexibility to the system. The process is as follows:

3. Fragmented Key Management (FKM) Module:

The FKM module is the central coordinator for managing the key fragments generated by both QEKD and DKG.  Its purpose is to combine these fragments into a complete OTP key only when needed for encryption or decryption.  This reduces storage requirements and minimizes the exposure time of the full key:

4. ASKA Integration and Overall Operation:

The QE-OTP module integrates seamlessly with other ASKA components:

This design ensures that the entire key generation and management process remains protected within the secure environment of the HESE-DAR, minimizing the risk of key compromise. The dynamic key generation and fragmented key management techniques further enhance efficiency and scalability.  The integration with ASKA's various modules provides comprehensive security and auditability.  The system also includes a fallback mechanism, the Isomorphic Legacy System Integration (ILSI), using classical post-quantum cryptography to support communication with systems that do not support quantum communication, ensuring backward compatibility without significant security trade-offs.

Independent Claims:

  1. A quantum-entangled one-time pad (OTP) module for secure communication within a secure computing architecture, comprising:

Dependent Claims:

  1. The quantum-entangled OTP module of claim 1, wherein the QEKD module utilizes entangled photon pairs for distributing the key fragments, such that any attempt to intercept a key fragment alters its state and alerts the communicating entities to the compromise.

  1. The quantum-entangled OTP module of claim 1, wherein the HESE-DAR further comprises a secure storage module configured to store the entangled key fragments securely until needed for key combination.

  1. The quantum-entangled OTP module of claim 1, wherein the integration module further interfaces with a multi-dimensional audit trail system (MDATS) to generate audit trails of key generation, distribution, and usage.

  1. The quantum-entangled OTP module of claim 1, further comprising an isomorphic legacy system integration (ILSI) module configured to interface with non-quantum-enabled devices using classical cryptographic methods as a fallback mechanism while minimizing security risks.

  1. The quantum-entangled OTP module of claim 5, wherein the ILSI module utilizes post-quantum cryptography (PQC) for secure communication with non-quantum-enabled devices.

  1. The quantum-entangled OTP module of claim 1, wherein the integration module further interfaces with an automated evolutionary software development system (AESDS) to receive software updates and security patches for the QEKD, DKG, and FKM modules.

  1. A method for secure communication within a secure computing architecture using a quantum-entangled OTP module, comprising the steps of:

  1. The method of claim 8, further comprising the step of decrypting the encrypted message using the complete OTP key at the receiving entity.

  1. A secure computing architecture comprising the quantum-entangled OTP module of claim 1.

This patent describes a novel One-Time Pad (OTP) module designed to address the limitations of traditional OTPs while seamlessly integrating with the ASKA architecture. It leverages quantum entanglement for secure key distribution and dynamic key generation, overcoming the key distribution and management challenges inherent in traditional OTP systems.

1. Core Concepts and Technologies:

2. Key Features and Functionality:

3. Key Relationships and Interactions:

Integration with ASKA:

This OTP module strengthens ASKA's security by providing a highly secure communication channel within and between IES instances and zones.  It specifically enhances:

This module can be deployed within HESE-DAR as a specialized chiplet (P12), managed and orchestrated by ASKA Hub and subject to decentralized governance rules.

Novelty and Advantages over Prior Art:

This patent proposes a novel approach to OTP implementation by:

  1. Using quantum entanglement for secure and instant key distribution, eliminating logistical challenges and vulnerabilities of traditional methods.
  2. Generating keys on-demand within a secure hardware enclave, minimizing storage requirements and the risk of key compromise.
  3. Leveraging fragmented key management to further enhance efficiency and security.
  4. Seamlessly integrating with ASKA, utilizing its architecture for strengthened defenses against numerous attack vectors.
  5. Providing a backward compatibility solution for non-quantum enabled devices via an isomorphic security stack that provides fallback to post-quantum cryptography (PQC)

Independent Claim 11 (Focus: Secure Read-Once with Verification):

  1. A quantum-entangled one-time pad (OTP) system for secure communication, comprising:

Independent Claim 12 (Focus: State-Based Key Fragment Progression):

  1. A quantum-entangled one-time pad (OTP) system for secure communication, comprising:

Explanation of Novelty and Addressing Real-World Challenges:

These independent claims introduce two novel approaches to managing the read-once nature of OTP keys and addressing the synchronization challenges inherent in distributed systems.

Claim 1 focuses on creating a verifiable read-once mechanism. It relies on generating a cryptographic proof that a key fragment has been accessed only once and subsequently destroyed on the storage medium. This proof can be exchanged between communicating parties to ensure that neither party attempts to reuse a key fragment. Importantly, the proof doesn't reveal the key fragment's value, preserving its secrecy.  This addresses both the read-once requirement and the synchronization challenge by providing a secure mechanism for verifying that a specific key fragment is used only once across the entire system.

Claim 2 introduces a state-based key fragment progression system.  By associating each key fragment with a unique, evolving quantum state, the system creates a built-in mechanism for tracking key usage.  The irreversible evolution of the quantum state acts as an intrinsic indicator of whether a fragment has been used. This approach eliminates the need for separate read verification and destruction mechanisms, as the quantum state itself provides the necessary information about key fragment usage.  The broadcast of the evolved state to all authorized entities further ensures synchronization, preventing reuse and maintaining the integrity of the OTP system.  This innovation leverages the inherent properties of quantum systems to simplify key management and synchronization.

These claims seek to offer practical solutions to the challenges of implementing true one-time pad systems, offering verifiable read-once, secure synchronization, and potentially simpler operation than traditional methods that rely on physical destruction or complex tracking mechanisms.  The use of quantum entanglement and quantum state evolution provides intrinsic security features that are difficult to replicate with classical systems.

BRAINSTORM PATENT 29:

The provided text focuses on optimizing and securing Non-Volatile Main Memory (NVMM) using encryption, particularly addressing the overhead of data shredding. While it doesn't directly address self-destructing one-time pads in the physical sense, it offers several relevant concepts that could be adapted for your Patent 29 QE-OTP module:

  1. Re-purposing Initialization Vectors (IVs):  Silent Shredder's core concept is to manipulate IVs in counter-mode encryption to render data unintelligible without physically overwriting the memory.  This could be adapted to your QE-OTP. Instead of physically destroying the pad, you could cryptographically "shred" it by changing a portion of the key or IV associated with that specific pad segment. This would make the pad unrecoverable without the original key/IV, effectively achieving a "virtual" self-destruction. The FKM (Fragmented Key Management) module from Patent 29 could be modified to manage this cryptographic shredding process.

  1. Counter Cache Invalidation:  Silent Shredder invalidates cached copies of shredded data. A similar approach can be used in your QE-OTP.  After a key fragment is used (and cryptographically shredded), invalidate any cached copies of that fragment across all IES instances. This prevents accidental or malicious reuse.

  1. Zeroing on Read (for specific use cases):  While not directly applicable to a self-destructing OTP, Silent Shredder's concept of returning zeros on a read of "shredded" data could be useful for specific applications within ASKA that might require zeroed memory initialization.  This wouldn't apply to the OTP itself but could be a useful feature for other ASKA services.

  1. Focus on Minimizing Writes:  The paper emphasizes the importance of minimizing writes to NVMM due to performance and endurance limitations. This reinforces the design choice of using fragmented keys in your QE-OTP (Patent 29), as it minimizes both storage requirements and the number of writes needed for key management.  Furthermore, the concept of cryptographic shredding aligns with minimizing physical writes, enhancing the longevity of the NVMM and reducing write-related power consumption, directly addressing one of the key challenges for NVMM deployment, as discussed in detail in the provided paper.  This emphasizes a significant advantage of the proposed approach.

  1. Security Considerations: The paper outlines several security concerns related to counter-mode encryption and provides potential mitigations (tampering with counter values, persistence of counter caches). These considerations are highly relevant to the design of your QE-OTP module, particularly for preventing replay attacks or unauthorized key access.  ASKA technologies such as the DTMS, the hierarchical security mesh (MSM), and the MDATS could be employed to address these vulnerabilities effectively. The emphasis on tamper-proofing counter values using techniques like Merkle trees is directly applicable to the protection of key fragments within the QE-OTP module. Integrating these robust integrity-checking mechanisms would significantly strengthen the overall security of the key management system, addressing potential vulnerabilities highlighted in the Silent Shredder paper.

By adapting these ideas, you can potentially design your QE-OTP with a cryptographically secure self-destruct mechanism for used pad fragments, reducing write overhead, enhancing security, and leveraging the existing ASKA infrastructure.  Remember that any cryptographic self-destruct mechanism must be carefully designed and analyzed to ensure it provides the required level of security and prevents any possibility of key recovery or pad reuse.  Thorough security analysis and testing are essential.

This text focuses on the challenges and solutions for achieving persistent security in Non-Volatile Memory (NVM) systems, especially those employing encryption and integrity protection. While it doesn't deal with quantum entanglement or one-time pads directly, the concepts discussed are highly relevant to the design and implementation of a secure, self-destructing QE-OTP module within the ASKA architecture. Here's a breakdown of the key takeaways:

  1. Persistent-Security: The paper introduces the concept of "Persistent-Security," emphasizing that security metadata (encryption counters, Merkle Tree data) must be persistently stored and recoverable to ensure secure recovery from crashes or power failures. This directly applies to your QE-OTP design.  The cryptographic "shredding" of used pad fragments must be a persistent operation. If the system crashes before the shredding is persisted, the system must be able to recover and complete the shredding process upon restart, ensuring that no key material can be recovered from the previously used pad fragments.  This aligns with the broader principle of crash consistency discussed in the text.

  1. Challenges of Persisting Security Metadata:  The text highlights the significant overhead associated with persisting security metadata, especially for integrity-protected systems using Merkle Trees. This reinforces the importance of optimizing your QE-OTP's key management (FKM module) to minimize the amount of metadata that needs to be persistently stored. The use of key fragments, as opposed to full-length keys, is a step in the right direction.

  1. Relaxation Schemes and Trade-offs: The paper discusses "relaxation schemes" for persisting security metadata, trading off performance against recovery time and resilience. This is a critical consideration for your QE-OTP design. While strict persistence of every key fragment operation is ideal for security, it could negatively impact performance.  You might need to explore relaxation schemes for non-critical key fragments or operations, carefully balancing security with performance.

  1. Secure Recovery of Non-Persistent Data:  The paper emphasizes the importance of protecting the security of non-persistent data even if its content isn't recoverable after a crash.  This principle applies to your QE-OTP. Even if some key fragments are designated as non-persistent (meaning their values aren't recoverable after a crash), you must still ensure that their associated counters or other metadata are managed securely to prevent reuse and maintain the integrity of the OTP system.

  1. Partitioning of Persistent and Non-Persistent Regions: The paper mentions how modern systems (like Linux) define persistent memory regions during boot. Your QE-OTP design could leverage this partitioning. Key fragments critical for recovery could be stored in the persistent region, while less critical fragments could reside in the non-persistent region, utilizing different keys for each region as suggested in the paper.  This reduces the burden of persisting all key fragment operations.

  1. Optimized Merkle Tree Persistence:  The paper discusses optimizing Merkle Tree persistence by storing only lower levels of the tree or using internal NVM registers within the processor.  This idea could be applied to your QE-OTP's integrity protection mechanisms.  Instead of persisting the entire Merkle Tree associated with the key fragments, you could explore storing only essential parts, reducing write overhead while maintaining sufficient information for recovery and verification.