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Differential private machine learning
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Differential-Privacy
Differential privacy learning and integration
1. Intuitive explanation
Differential privacy explaining
Differential Privacy - Simply Explained
Differential Privacy A Primer for a Non-technical Audience
Differential privacy introduction Reading (not so much mathematics, but intuition)
Laplacian Noisy Counting mechanism illustratioin
2. Academic paper
2.1 Survey
The Algorithm foundation of Differential Privacy
The Algorithm foundation of Differential privacy
The Algorithmic Foundations of Privacy Reading community
Differential privacy and application
differential privacy and application
Differential privacy and application Reading Notes
The complexity of differential privacy
机器学习隐私保护研究综述-谭作文
Differentially Private Data Publishing and Analysis: a Survey
Repository of paper on Differential Privacy
Paper of Differential Privacy in CCS, S&P, NDSS, USENIX, Infocom
SoK: Differential Privacies
2.2 Course
Seminar on differential privacy, Fall 19/20
CSE 660 Fall 2017
cs295-data-privacy
Privacy Study Group
CS 860 - Algorithms for Private Data Analysis - Fall 2020
2.3 some mechanisms
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
2.4 Differential Privacy in CCS, S&P, NDSS, USENIX, Infocom from 2015-2019 (some of them are from 2020)
Survey
3. Video
Recent Development in Differential Privacy II
Recent Development in Differential Privacy I
Privacy Amplification by Sampling and Renyi Differential Privacy
Differential Privacy: From Theory to Practice
4. Code
4.0 代码实现DP算法
4.1 K-Anonymity Algorithm
4.2 Randomized response
4.3 Laplace and Exponential Mechanism
4.4 Gaussian Mechanism
4.5 Google Differential Privacy Library
4.6 IBM Differential Privacy Library
4.7 Facebook pytorch-dp: Train PyTorch models with Differential Privacy
4.8 differential-privacy-federated-learning
4.9 PySyft: A library for encrypted, privacy preserving machine learning
4.10 PyGrid: A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science
4.11 PyVacy: Privacy Algorithms for PyTorch
4.12 DP-XGBoost: A DP fork of the famous scalable ML engine
Open Source Agenda is not affiliated with "Billy1900 Awesome Differential Privacy" Project. README Source:
Billy1900/Awesome-Differential-Privacy
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Last Commit
2 years ago
Repository
Billy1900/Awesome-Differential-Privacy
License
MIT
Homepage
https://github.com/Billy1900/Differential-Privacy
Tags
Differential Privacies
Differential Privacy
Differential Privacy Learning
Differentially Private
Dpsgd
Privacy Preserving
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