b033923756
- Remove executable permissions from configuration files (.editorconfig, .env.example, .gitignore) - Remove executable permissions from documentation files (README.md, LICENSE, SECURITY.md) - Remove executable permissions from web assets (HTML, CSS, JS files) - Remove executable permissions from data files (JSON, SQL, YAML, requirements.txt) - Remove executable permissions from source code files across all apps - Add executable permissions to Python
76 lines
1.8 KiB
Markdown
76 lines
1.8 KiB
Markdown
# FHE Integration Plan for AITBC
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## Candidate Libraries
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### 1. Microsoft SEAL (C++ with Python bindings)
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**Pros:**
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- Mature and well-maintained
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- Supports both BFV and CKKS schemes
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- Good performance for ML operations
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- Python bindings available
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- Extensive documentation
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**Cons:**
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- C++ dependency complexity
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- Larger binary size
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- Steeper learning curve
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**Use Case:** Heavy computational ML workloads
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### 2. TenSEAL (Python wrapper for SEAL)
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**Pros:**
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- Pure Python interface
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- Built on top of SEAL
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- Easy integration with existing Python codebase
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- Good for prototyping
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**Cons:**
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- Performance overhead
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- Limited to SEAL capabilities
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- Less control over low-level operations
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**Use Case:** Rapid prototyping and development
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### 3. Concrete ML (Python)
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**Pros:**
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- Designed specifically for ML
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- Supports neural networks
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- Easy model conversion
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- Good performance for inference
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**Cons:**
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- Limited to specific model types
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- Newer project, less mature
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- Smaller community
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**Use Case:** Neural network inference on encrypted data
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## Recommended Approach: Hybrid ZK + FHE
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### Phase 1: Proof of Concept with TenSEAL
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- Start with TenSEAL for rapid prototyping
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- Implement basic encrypted inference
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- Benchmark performance
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### Phase 2: Production with SEAL
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- Migrate to SEAL for better performance
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- Implement custom optimizations
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- Integrate with existing ZK circuits
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### Phase 3: Specialized Solutions
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- Evaluate Concrete ML for neural networks
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- Consider custom FHE schemes for specific use cases
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## Integration Architecture
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```
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Client Request → ZK Proof Generation → FHE Computation → ZK Result Verification → Response
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```
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### Workflow:
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1. Client submits encrypted ML request
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2. ZK circuit proves request validity
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3. FHE computation on encrypted data
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4. ZK circuit proves computation correctness
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5. Return encrypted result with proof
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