privacy-preserving-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

A curated list of awesome responsible machine learning resources.

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

Training PyTorch models with differential privacy

A Privacy-Preserving Framework Based on TensorFlow

Privacy Testing for Deep Learning

Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

Advanced Privacy-Preserving Federated Learning framework

Implementation of protocols in SecureNN.

Fast, memory-efficient, scalable optimization of deep learning with differential privacy

Piranha: A GPU Platform for Secure Computation

Implementation of protocols in Falcon

Full stack service enabling decentralized machine learning on private data

This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)

Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference

[ICML 2022 / ICLR 2024] Source code for our papers "Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks" and "Be Careful What You Smooth For".

This repository contains all the implementation of different papers on Federated Learning

Privacy-Preserving Machine Learning (PPML) Tutorial

Secure Linear Regression in the Semi-Honest Two-Party Setting.