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

Master Federated Learning in 2 Hours—Run It on Your PC!

Training PyTorch models with differential privacy

Database anonymization and synthetic data generation tool

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 SDK ✨

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

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. )

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

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

Simulate a federated setting and run differentially private federated learning.

Paper notes and code for differentially private machine learning

Simulation framework for accelerating research in Private Federated Learning

Repository for collection of research papers on privacy.

Differential privacy validator and runtime