Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.

Develop on and interact with BasedAI.

Official mirror of Python-FHEz; Python Fully Homomorphic Encryption (FHE) Library for Encrypted Deep Learning as a Service (EDLaaS).

Python implementation of the Fully Homomorphic Encryption Scheme TFHE

Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated

Fully Homomorphic Encryption for Private Federated Learning

Official code for "DCT-CryptoNets: Scaling Private Inference in the Frequency Domain" [ICLR 2025]

Cifer provides a decentralized AI development ecosystem with data-ownership proof on the Cifer blockchain, using a Privacy-Preserving Machine Learning (PPML) framework offers several methods for secure, private, collaborative machine learning “Federated Learning” and “Fully Homomorphic Encryption”

The first one in the World fast, secure and practical Fully Homomorphic Encryption (FHE) cryptosystem. So far, here I place only demo samples, since all the algorithms and implementation are proprietary

This is an attempt to use BFV-FHE scheme for image encryption using an open-source implementation of the same.

Experiments in using Z3 to check common FHE transformations

MPC key storage experiments for various FHE cryptosystems using Nillion's nilDB

Fully Homomorphic Encryption and neural networks experiments

Easily run the Zama project by clicking run_sepolia.bat. Enter your private key in the GUI, adjust the claim time, and start claiming! 🚀💻