A unified framework for privacy-preserving data analysis and machine learning
-
Updated
May 15, 2024 - Python
A unified framework for privacy-preserving data analysis and machine learning
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
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.
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. )
Tools and service for differentially private processing of tabular and relational data
机器学习和差分隐私的论文笔记和代码仓
Privacy Engineering Collaboration Space
Simulation framework for accelerating research in Private Federated Learning
Cross-silo Federated Learning playground in Python. Discover 7 real-world federated datasets to test your new FL strategies and try to beat the leaderboard.
Differentially Private Optimization for PyTorch 👁🙅♀️
Differential private machine learning
A codebase that makes differentially private training of transformers easy.
A toolbox for differentially private data generation
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
A Simulator for Privacy Preserving Federated Learning
Code for ML Doctor
Python language bindings for smartnoise-core.
Add a description, image, and links to the differential-privacy topic page so that developers can more easily learn about it.
To associate your repository with the differential-privacy topic, visit your repo's landing page and select "manage topics."