Fit interpretable models. Explain blackbox machine learning.

Google's differential privacy libraries.

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

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

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

Training PyTorch models with differential privacy

Database anonymization and synthetic data generation tool

Diffprivlib: The IBM Differential Privacy Library

Synthetic Data SDK ✨

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

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

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

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. )

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.