0.1 Technical Requirements

Version: 1.0

1. System Overview

The system enables secure data contribution and usage for AI model training through a two-tier architecture: private (encrypted) and public (obfuscated) data storage with secure model weight conversion.

2. Data Flow Architecture

2.1 User Contributor Flow

graph TD
    A[User Device] -->|Upload| B[Server]
    B -->|Process & Validate| C{Data Processing}
    C -->|Version 1| D[Private Storage]
    C -->|Version 2| E[Public Storage]
    D -->|Encrypt| F[(Private Database)]
    E -->|Obfuscate| G[(Public Dataset)]

Processing Steps:

Data Upload
- Secure file transfer protocol
- Client-side checksum verification
- Rate limiting and size restrictions
Data Validation
- Schema validation
- Data quality checks
- Format standardization
- Sanitization
Dual Version Creation
- Private Version (V1):
  - Encryption using industry-standard algorithms
  - Secure key management
  - Versioning and audit trails
- Public Version (V2):
  - Reversible obfuscation
  - Privacy preservation
  - Structural integrity maintenance

2.2 Data User Flow

graph TD
    A[Data User] -->|Download| B[Public Dataset]
    B -->|Train| C[Local Model]
    C -->|Submit Weights| D[TEE]
    D -->|Convert| E[Real Weights]
    F[(Private Data)] -.->|Reference| D

3. Technical Requirements

3.1 Data Obfuscation Requirements

Must preserve data relationships and patterns
Must be computationally reversible within TEE
Must provide differential privacy guarantees
Must maintain data utility for ML training

Candidate Techniques:

Feature Transformation
- Dimensionality reduction
- Random projection
- Noise injection
Differential Privacy
- ε-differential privacy
- Local sensitivity
- Composition theorems

3.2 Storage Requirements

Private Storage (PostgreSQL)

Encrypted at rest
Column-level encryption
Partitioning strategy for large datasets
Backup and recovery procedures

Schema Design:

CREATE TABLE private_datasets (
    id UUID PRIMARY KEY,
    owner_id UUID,
    encrypted_data BYTEA,
    encryption_metadata JSONB,
    created_at TIMESTAMP,
    version INTEGER
);

Public Storage

Requirements:

High availability
Global distribution
Cost-effective for large datasets
Versioning support

Options:

CDN-backed Object Storage
- Amazon S3 + CloudFront
- Google Cloud Storage + Cloud CDN
API-based Access
- GraphQL API for selective data access
- REST API for bulk downloads
- WebSocket for real-time updates

3.3 TEE (Trusted Execution Environment) Requirements

Secure enclave support (Intel SGX/AMD SEV)
Remote attestation
Secure key management
Memory encryption
Secure I/O

4. Technical Priorities

4.1 Phase 1: Core Infrastructure

ML Obfuscation Technique Selection
- Success Criteria:
  - Reversible transformation
  - Privacy guarantees
  - Performance benchmarks
  - Memory efficiency
Data Delivery Architecture
- Success Criteria:
  - Download speeds >100MB/s
  - 99.99% availability
  - Global latency <100ms
  - Cost per GB optimization
Storage Implementation
- Success Criteria:
  - Query performance
  - Encryption overhead
  - Backup/recovery times
  - Scaling capabilities

4.2 Phase 2: Security Hardening

Access Control System
Audit Logging
Key Rotation
Threat Monitoring

4.3 Phase 3: Performance Optimization

Caching Strategy
Query Optimization
Network Optimization
Resource Scaling

5. Risk Assessment

Data Privacy
- Risk: Re-identification through pattern analysis
- Mitigation: Regular privacy audits, differential privacy guarantees
System Security
- Risk: TEE vulnerabilities
- Mitigation: Regular security updates, hardware attestation
Performance
- Risk: Scaling issues with large datasets
- Mitigation: Distributed processing, efficient storage design

6. Success Metrics

Security
- Zero data breaches
- 100% encryption coverage
- Regular security audit compliance
Performance
- <5s data processing time
- <1s weight conversion time
- 99.99% system uptime
Scalability
- Support for 10,000+ concurrent users
- Handle datasets up to 100GB
- Linear cost scaling

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