Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 26, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Google's differential privacy libraries.
A unified framework for privacy-preserving data analysis and machine learning
Training PyTorch models with differential privacy
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study
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.
The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
Simulate a federated setting and run differentially private federated learning.
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
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. )
The core library of differential privacy algorithms powering the OpenDP Project.
Differential privacy validator and runtime
机器学习和差分隐私的论文笔记和代码仓
Tools and service for differentially private processing of tabular and relational data
Privacy Engineering Collaboration Space
Simulation framework for accelerating research in Private Federated Learning
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