aitbc/apps/zk-circuits/fhe_integration_plan.md

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:

1. Client submits encrypted ML request
2. ZK circuit proves request validity
3. FHE computation on encrypted data
4. ZK circuit proves computation correctness
5. Return encrypted result with proof