# 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
