Differential privacy learning and integration
- 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
- The Algorithm foundation of Differential Privacy
- Differential privacy and application
- 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
- 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
2.4 Differential Privacy in CCS, S&P, NDSS, USENIX, Infocom from 2015-2019 (some of them are from 2020)
- 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
- 4.0 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