Fit interpretable models. Explain blackbox machine learning.

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

Google's differential privacy libraries.

Training PyTorch models with differential privacy

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

We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study

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

Diffprivlib: The IBM Differential Privacy Library

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

Privacy Engineering Collaboration Space

Simulate a federated setting and run differentially private federated learning.

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

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

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

Repository for collection of research papers on 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. )

Code samples and documentation for SmartNoise differential privacy tools

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

Contains interesting projects like Cat face detection, cat face recognition, code generation, Building chatbot, finding similar documents, image segmentation, UCI credit card, anomaly detection, MNIST etc.

Differential private machine learning