PrivNet.AI - Privacy-Preserving Geometric Deep Learning

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.

🚀 Project Vision

Build a privacy-centric infrastructure where users can train graph-based models without ever decrypting their data.

We use isogeny-based cryptography (post-quantum secure) and graph neural networks (GNNs) to perform secure, structure-aware learning on encrypted data.

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🧩 Key Technologies

• Post-Quantum Cryptography using Isogeny Graphs
• Graph Neural Networks for structured data learning
• SageMath for elliptic curve operations
• PyTorch Geometric / DGL for GNN modeling
• Federated Learning & Differential Privacy (future integration)

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🔧 Project Modules

📦 privnet-ai/
├── crypto/             # Post-quantum cryptography (Isogeny-based)
├── models/             # GNN and secure architectures
├── data/               # Encrypted sample datasets
├── utils/              # Tools for encryption, graph building, etc.
├── notebooks/          # Research + demo notebooks
├── docs/               # Technical documentation
├── .github/            # PR templates, issue templates
├── CONTRIBUTING.md     # How to contribute
├── CODE_OF_CONDUCT.md  # Collaboration guidelines
├── roadmap.md          # Project vision and goals
└── README.md           # Project intro and contribution guide

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🧠 Why this project matters?

Current 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.

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🛠️ Getting Started

1. Clone the repo

git clone https://github.com/chimans/privnet-ai.git
cd privnet-ai

2. Setup environment

conda create -n privnet python=3.11
conda activate privnet
pip install -r requirements.txt

3. Explore the notebooks

jupyter notebook notebooks/demo_secure_gnn.ipynb

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📚 Documentation

• docs/crypto_intro.md – Post-quantum cryptography primer
• docs/gnn_architecture.md – Geometric learning modules
• docs/contribute.md – Contribution guide

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👥 How to Contribute

We welcome contributions from cryptographers, ML engineers, researchers, and developers.

Ways to contribute:

• Build core GNN modules or crypto components
• Suggest ideas and improve documentation
• Review or test notebooks and demos
• Translate or improve accessibility

📌 Contribution checklist:

1. Fork this repo
2. Create a new feature branch: git checkout -b feature/your-feature
3. Make your changes with clear commits
4. Open a pull request and fill out the PR template

Pull Request Template:

### What does this PR do?
- Clearly explain your update/fix

### Checklist:
- [ ] My code follows the project style
- [ ] I’ve tested this locally
- [ ] I linked any related Issue

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📍 Project Status

We’re in early development — building the prototype (crypto backend + GNN inference on encrypted graphs). Use Issues to suggest features or Discussions to brainstorm with us.

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🗺️ Roadmap Highlights (see roadmap.md for full list)

• Repo bootstrapping & structure setup
• Crypto layer: isogeny encryption modules (basic)
• GNN core on synthetic data
• Privacy-preserving inference pipeline
• MVP deployment + cloud interface

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📜 License

MIT License — free to use, modify, and contribute.

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✨ Contact & Community

• File GitHub Issues or PRs
• Community chat (coming soon: Discord/Matrix)
• Follow roadmap.md for what's coming next

Let's build privacy-native AI together.

— The PrivNet.AI team