#计算机科学#Fit interpretable models. Explain blackbox machine learning.
Google's differential privacy libraries.
#计算机科学#A unified framework for privacy-preserving data analysis and machine learning
#计算机科学#Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
Master Federated Learning in 2 Hours—Run It on Your PC!
#计算机科学#Training PyTorch models with differential privacy
#计算机科学#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.
#计算机科学#Synthetic Data SDK ✨
The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
#计算机科学#Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
#计算机科学#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. )
#计算机科学#Simulate a federated setting and run differentially private federated learning.
The core library of differential privacy algorithms powering the OpenDP Project.
#计算机科学#Paper notes and code for differentially private machine learning
#Awesome#Repository for collection of research papers on privacy.
#计算机科学#Simulation framework for accelerating research in Private Federated Learning
Tools and service for differentially private processing of tabular and relational data