G-PATE This is the official code base for our NeurIPS 2021 paper: "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of T

federated is the source code for the Bachelor's Thesis Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federat

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

private-transformers This codebase facilitates fast experimentation of differentially private training of Hugging Face transformers. What is this? Why

private-transformers This codebase facilitates fast experimentation of differentially private training of Hugging Face transformers. What is this? Why

InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig

Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the cli

Differential Privacy (DP) Based Federated Learning (FL) Everything about DP-based FL you need is here. （所有你需要的DP-based FL的信息都在这里） Code Tip: the code o