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Core Concepts Overview

Platform Architecture

AIDDDMAP is built on three fundamental pillars:

  1. Data Sovereignty & Privacy

  2. End-to-end encryption

  3. Homomorphic encryption capabilities
  4. Zero-knowledge proofs

  5. User-controlled data sharing

  6. AI Agent Ecosystem

  7. Multi-agent collaboration

  8. Specialized roles and capabilities
  9. Real-time interaction and learning

  10. Universal deployment framework

  11. Interactive Tools & Interfaces

  12. IDAT (Interactive Data Assembly Tool)
  13. AI Agent Dashboard
  14. Encryption Drawer
  15. Live Data Handler

Key Components

1. Multi-Agent System

The platform employs seven specialized AI agents:

graph TD
    A[Steering Librarian] --> B[DataFinder AI]
    A --> C[DataCurator AI]
    A --> D[ValuationAI]
    A --> E[TrendAnalysis AI]
    A --> F[Publishing AI]
    A --> G[Data Quality Assessment AI]

Each agent has specific responsibilities:

  • DataFinder AI: Discovers relevant datasets
  • DataCurator AI: Prepares and enhances data
  • ValuationAI: Determines fair market value
  • TrendAnalysis AI: Analyzes market patterns
  • Publishing AI: Manages publication process
  • Data Quality Assessment AI: Evaluates dataset quality
  • Steering Librarian: Coordinates all agents

2. Universal Agent Deployment Module (UADM)

interface UADMConfig {
  hardwareAbstraction: {
    protocols: string[]; // Supported communication protocols
    deviceTypes: string[]; // Supported device categories
  };
  security: {
    encryption: boolean; // Enable/disable encryption
    permissions: string[]; // Required permissions
  };
  simulation: {
    enabled: boolean; // Enable simulation mode
    environment: string; // Simulation environment type
  };
}

The UADM provides:

  • Hardware abstraction layer
  • Protocol support (WiFi, Bluetooth, MQTT)
  • Simulation environments
  • Security frameworks

3. Interactive Data Assembly Tool (IDAT)

Core features include:

  • Visual workflow creation
  • Node-based data processing
  • Real-time agent interaction
  • Live data visualization

4. Encryption Systems

Three primary encryption modes:

  1. FHE (Fully Homomorphic Encryption)
interface FHEConfig {
  scheme: "CKKS" | "BFV" | "BGV";
  parameters: {
    polyModulusDegree: number;
    coeffModulusBits: number[];
  };
}
  1. ZK (Zero-Knowledge)
interface ZKConfig {
  proofSystem: "Groth16" | "Plonk";
  curve: "BN254" | "BLS12-381";
}
  1. Basic Encryption
interface BasicEncryption {
  algorithm: "AES-256-GCM" | "ChaCha20-Poly1305";
  keySize: number;
}

System Architecture

graph TB
    subgraph Frontend
        UI[User Interface]
        IDAT[IDAT Canvas]
        AD[Agent Dashboard]
    end

    subgraph Core
        AM[Agent Manager]
        UADM[UADM]
        EM[Encryption Module]
    end

    subgraph Backend
        DB[(Database)]
        BC[Blockchain]
        S3[Storage]
    end

    UI --> AM
    IDAT --> AM
    AD --> AM
    AM --> UADM
    AM --> EM
    UADM --> DB
    EM --> S3
    AM --> BC

Integration Points

1. External Systems

  • Blockchain networks
  • IoT devices
  • Data sources
  • AI model repositories

2. Internal Communication

  • WebSocket for real-time updates
  • REST APIs for CRUD operations
  • Event-driven architecture

3. Storage Systems

  • Distributed file system
  • Blockchain storage
  • Encrypted databases

Best Practices

  1. Security

  2. Always encrypt sensitive data

  3. Implement role-based access control

  4. Regular security audits

  5. Performance

  6. Optimize large data operations

  7. Use caching strategies

  8. Implement lazy loading

  9. Scalability

  10. Design for horizontal scaling
  11. Use microservices architecture
  12. Implement load balancing

Next Steps