- Tensorflow Privacy
- A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
- Google's Differential Privacy
- This is a C++ library of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.
- Intel Homomorphic Encryption Backend
- The Intel HE transformer for nGraph is a Homomorphic Encryption (HE) backend to the Intel nGraph Compiler, Intel's graph compiler for Artificial Neural Networks.
- Microsoft SEAL
- Microsoft SEAL is an easy-to-use open-source (MIT licensed) homomorphic encryption library developed by the Cryptography Research group at Microsoft. (Simple Encrypted Arithmetic Library or SEAL)
- PySyft
- A Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Secure Multi-Party Computation (MPC) within PyTorch. (PySyft is managed by OpenMined community.)
- Substra
- Substra is an open-source framework for privacy-preserving, traceable and collaborative Machine Learning.
- TF Encrypted / TF_SEAL
- A Framework for Confidential Machine Learning on Encrypted Data in TensorFlow.
- Uber SQL Differencial Privacy
- Uber's open source framework that enforces differential privacy for general-purpose SQL queries.
