Real, working examples for Fully Homomorphic Encryption development.
Production-ready machine learning on encrypted data:
| Example | Description | Data |
|---|---|---|
| credit_scoring/ | Credit risk prediction | HMEQ dataset |
| disease_prediction/ | Healthcare predictions | Medical records |
| cifar/ | Image classification | CIFAR-10 |
| federated_learning/ | Distributed training | - |
| hybrid_model/ | Mixed FHE/plaintext | - |
cd ml/credit_scoring
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
jupyter lab CreditScoring.ipynbProduction deployment patterns:
| Example | Description |
|---|---|
| breast_cancer/ | Cancer detection API |
| sentiment_analysis/ | NLP on encrypted text |
| server/ | FHE inference server |
FHE for smart contracts:
| Example | Description |
|---|---|
| hardhat-template/ | Solidity + FHE |
| react-template/ | React dApp + FHE |
- Python 3.10+
- For ML:
pip install torus-ml - For blockchain: Node.js 18+
- luxfhe/docs - Documentation
- luxfhe/handbook - Deep dive
- luxfhe/workshop - Tutorials
Apache 2.0