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 ✨

Benchmark of federated learning. Dedicated to the community. 🤗

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.

Differentially private federated learning: A systematic review (ACM Survey); Adap dp-fl: Differentially private federated learning with adaptive noise (TrustCom'2022)

Simulation framework for accelerating research in Private Federated Learning

Repository for collection of research papers on privacy.