Practical Homomorphic Encryption for Machine Learning Inference
Homomorphic encryption allows computation on encrypted data. We've made it practical for ML inference.
The Promise of HE
Imagine:
- Cloud inference without data exposure
- Privacy-preserving analytics
- Secure multi-party computation
- Regulatory compliance by design
The Challenge
Traditional HE is impractical:
- 1000x slower than plaintext
- Massive ciphertext expansion
- Limited supported operations
- Complex programming model
Our Innovations
Optimized Bootstrapping
- 10x faster refresh operations
- Automatic parameter selection
- Noise management optimization
ML-Specific Optimizations
- Polynomial activation approximations
- SIMD packing strategies
- Hybrid plaintext/ciphertext computation
- Operator fusion
Compiler Infrastructure
- High-level ML framework integration
- Automatic optimization passes
- Hardware acceleration support
Performance
| Model | Plaintext | HE (Ours) | Slowdown |
|---|---|---|---|
| MNIST CNN | 0.1ms | 2.3ms | 23x |
| ResNet-18 | 15ms | 180ms | 12x |
| BERT-base | 45ms | 890ms | 20x |
Deployment
Production systems running:
- Medical diagnosis (3 hospitals)
- Financial fraud detection (2 banks)
- Biometric authentication (1M users)