Welcome to PrivNet.AI — an open-source platform combining post-quantum cryptography and geometric deep learning to enable privacy-preserving machine learning on sensitive data such as genomics, financial networks, and healthcare records.
Build a privacy-centric infrastructure where users can train graph-based models without ever decrypting their data.
We aim to use isogeny-based cryptography (post-quantum secure) and graph neural networks (GNNs) to perform secure, structure-aware learning on encrypted data.
Current Status: This is a proof-of-concept (PoC) demonstrating encrypted linear operations on graph neural network features. The current implementation uses
EncryptionShim(a cryptographic simulator) for demonstration purposes. See Limitations below.
- Graph Neural Networks for structured data learning (PyTorch Geometric)
- Post-Quantum Cryptography (PQC) modules for future isogeny-based encryption
- Homomorphic Encryption operations framework (in development)
- PyTorch for deep learning infrastructure
- Federated Learning & Differential Privacy (planned for future integration)
📦 privnet-ai/
├── crypto/ # Cryptographic modules
│ ├── encryption.py # EncryptionShim (PoC simulator)
│ ├── homomorphic/ # Homomorphic encryption operations
│ ├── pqc/ # Post-quantum cryptography (Kyber)
│ └── protocols/ # Cryptographic protocols
├── ml/ # Machine learning modules
│ ├── models/ # GNN models (GCN, etc.)
│ ├── layers/ # Custom GNN layers
│ ├── training/ # Training utilities
│ └── geometric/ # Geometric learning components
├── core/ # Core utilities
│ ├── config.py # Configuration management
│ └── utils.py # General utilities
├── data/ # Datasets (Cora, CiteSeer, PubMed)
├── api/ # API endpoints (FastAPI)
├── tests/ # Test suite
├── docs/ # Documentation
├── scripts/ # Utility scripts
├── deployment/ # Deployment configurations
├── CONTRIBUTING.md # How to contribute
├── CODE_OF_CONDUCT.md # Collaboration guidelines
├── roadmap.md # Project vision and goals
└── README.md # This fileCurrent ML systems expose data at many points: during training, inference, or transport. This is not acceptable for sensitive data (e.g., genome sequences, health records, financial transactions).
PrivNet.AI introduces a new paradigm: Train on encrypted data. Analyze graphs with security. Scale with structure.
- Python 3.11+ (tested with 3.12+)
- pip or conda
- Clone the repository
git clone https://github.com/chimans/privnet-ai.git
cd privnet-ai- Create a virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies
pip install --upgrade pip setuptools wheel
pip install -r requirements.txtTrain a Graph Convolutional Network (GCN) on Planetoid datasets and evaluate encrypted linear operations:
# Train on Cora dataset with encrypted linear head evaluation
python main.py --dataset cora --epochs 50 --enc_eval
# Available options:
# --dataset {cora|citeseer|pubmed} # Choose dataset (default: cora)
# --epochs N # Training epochs (default: 100)
# --hidden N # Hidden dimension (default: 64)
# --lr FLOAT # Learning rate (default: 0.01)
# --device {auto|cpu|cuda} # Device selection (default: auto)
# --enc_eval # Enable encrypted linear head evaluation
# --enc_scale FLOAT # Quantization scale for EncryptionShim (default: 256.0)
# --seed N # Random seed (default: 42)Epoch 001 | loss=1.9463 | train=0.229 | val=0.138 | test=0.158
...
Epoch 050 | loss=0.5108 | train=0.979 | val=0.806 | test=0.831
=== Final ===
Device: cpu
Dataset: Cora
Train Acc: 0.979
Val Acc: 0.810
Test Acc: 0.827
Time (sec): 0.6
Encrypted linear head test acc: 0.492
- roadmap.md – Project roadmap and development phases
- README_INSTRUCTION.md – Detailed PoC documentation
- CONTRIBUTING.md – Contribution guidelines
- CODE_OF_CONDUCT.md – Community guidelines
The current PoC demonstrates:
- GCN Training: A two-layer Graph Convolutional Network trained on Planetoid datasets (Cora, CiteSeer, PubMed)
- Feature Extraction: Hidden representations (
H) are extracted after the first GCN layer - Linear Head: A ridge regression-based linear classifier is trained on plaintext features
- Encrypted Evaluation: The linear head is applied to encrypted/masked features using
EncryptionShim
The EncryptionShim is not real cryptography. It's a proof-of-concept simulator that:
- Quantizes floating-point values to fixed-point integers
- Applies random additive masking
- Supports linear operations (addition, scalar multiplication, matrix multiplication)
- Demonstrates the feasibility of linear computation on masked data
Important: This is a simulation for demonstration purposes only. Real cryptographic security requires proper homomorphic encryption schemes.
EncryptionShimis not cryptographically secure — it's a quantization + masking simulator- Only linear operations are supported; nonlinearities (ReLU, Softmax) cannot be executed in the masked domain
- Graph structure and node features are processed in plaintext during training
- No post-quantum resistance or zero-knowledge proofs implemented
- Feature extraction (
H) happens in plaintext; only the linear head operates on masked data
See README_INSTRUCTION.md for detailed technical documentation.
Run the test suite to verify the installation:
# Run all tests
pytest
# Run with coverage
pytest --cov=. --cov-report=html
# Run specific test files
pytest tests/test_encryption.py
pytest tests/test_models.py
pytest tests/test_integration.pyWe welcome contributions from cryptographers, ML engineers, researchers, and developers.
- Build core GNN modules or crypto components
- Implement real homomorphic encryption schemes (CKKS, BFV)
- Improve documentation and add examples
- Write tests and improve test coverage
- Review code and suggest improvements
- Report bugs and suggest features
- Fork this repo
- Create a new feature branch:
git checkout -b feature/your-feature - Make your changes with clear commits
- Add tests for new functionality
- Ensure all tests pass:
pytest - Open a pull request and fill out the PR template
### What does this PR do?
- Clearly explain your update/fix
### Checklist:
- [ ] My code follows the project style
- [ ] I've tested this locally
- [ ] I added tests for new functionality
- [ ] I linked any related IssueWe're in early development — currently building a proof-of-concept demonstrating encrypted linear operations on GNN features.
Current Phase: PoC with EncryptionShim simulator Next Steps: See roadmap.md for detailed development phases
Use Issues to suggest features or Discussions to brainstorm with us.
See roadmap.md for the complete development plan.
Completed:
- Repo bootstrapping & structure setup
- PoC with EncryptionShim and GCN on Planetoid datasets
- Basic test suite
In Progress / Planned:
- Real homomorphic encryption integration (CKKS/BFV)
- Full encrypted inference pipeline
- Post-quantum cryptography (isogeny-based)
- Encrypted graph structure support
- Federated learning integration
- MVP deployment
MIT License — free to use, modify, and contribute.
- Discord: Join our community
- GitHub Issues: Report bugs, request features, or ask questions
- Contributions: See CONTRIBUTING.md for guidelines
- Roadmap: Follow roadmap.md for development progress
Let's build privacy-native AI together.
— The PrivNet.AI team
Special thanks to the open-source communities behind PyTorch, PyTorch Geometric, and the cryptographic research community working on privacy-preserving machine learning.