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

A Fully Homomorphic Encryption (FHE) library for bridging the gap between theory and practice with a focus on performance and accuracy.

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

A depth-aware secure computation compiler

Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated

several implementations of the number theoretic transform (NTT)

Experiments in using Z3 to check common FHE transformations