IBM/differential-privacy-library

Google's differential privacy libraries.

Library for training machine learning models with privacy for training data

Training PyTorch models with differential privacy

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

Perform data science on data that remains in someone else's server

A Framework for Encrypted Machine Learning in TensorFlow

Concrete: TFHE Compiler that converts python programs into FHE equivalent

GPU-accelerated homomorphic encryption in Python: Paillier and BFV, with encrypted nearest-neighbour search. Experimental research code.

An Open Framework for Federated Learning.