Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 3, 2024 - C++
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.
Differential private machine learning
Privacy-preserving generative deep neural networks support clinical data sharing
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