15427c96c0
- Change file mode from 644 to 755 for all project files - Add chain_id parameter to get_balance RPC endpoint with default "ait-devnet" - Rename Miner.extra_meta_data to extra_metadata for consistency
1.8 KiB
Executable File
1.8 KiB
Executable File
FHE Integration Plan for AITBC
Candidate Libraries
1. Microsoft SEAL (C++ with Python bindings)
Pros:
- Mature and well-maintained
- Supports both BFV and CKKS schemes
- Good performance for ML operations
- Python bindings available
- Extensive documentation
Cons:
- C++ dependency complexity
- Larger binary size
- Steeper learning curve
Use Case: Heavy computational ML workloads
2. TenSEAL (Python wrapper for SEAL)
Pros:
- Pure Python interface
- Built on top of SEAL
- Easy integration with existing Python codebase
- Good for prototyping
Cons:
- Performance overhead
- Limited to SEAL capabilities
- Less control over low-level operations
Use Case: Rapid prototyping and development
3. Concrete ML (Python)
Pros:
- Designed specifically for ML
- Supports neural networks
- Easy model conversion
- Good performance for inference
Cons:
- Limited to specific model types
- Newer project, less mature
- Smaller community
Use Case: Neural network inference on encrypted data
Recommended Approach: Hybrid ZK + FHE
Phase 1: Proof of Concept with TenSEAL
- Start with TenSEAL for rapid prototyping
- Implement basic encrypted inference
- Benchmark performance
Phase 2: Production with SEAL
- Migrate to SEAL for better performance
- Implement custom optimizations
- Integrate with existing ZK circuits
Phase 3: Specialized Solutions
- Evaluate Concrete ML for neural networks
- Consider custom FHE schemes for specific use cases
Integration Architecture
Client Request → ZK Proof Generation → FHE Computation → ZK Result Verification → Response
Workflow:
- Client submits encrypted ML request
- ZK circuit proves request validity
- FHE computation on encrypted data
- ZK circuit proves computation correctness
- Return encrypted result with proof