Fit interpretable models. Explain blackbox machine learning.

Google's differential privacy libraries.

A unified framework for privacy-preserving data analysis and machine learning

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

Training PyTorch models with differential privacy

37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.

Diffprivlib: The IBM Differential Privacy Library

OpenHuFu is an open-sourced data federation system to support collaborative queries over multi databases with security guarantee.

Synthetic data generators for structured and unstructured text, featuring differentially private learning.

The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

Simulate a federated setting and run differentially private federated learning.

Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )

Repository for collection of research papers on privacy.

The core library of differential privacy algorithms powering the OpenDP Project.

机器学习和差分隐私的论文笔记和代码仓

Simulation framework for accelerating research in Private Federated Learning

Differential privacy validator and runtime

Tools and service for differentially private processing of tabular and relational data

Privacy Engineering Collaboration Space