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https://github.com/menisadi/awesome-differential-privacy
list of differential-privacy related resources
https://github.com/menisadi/awesome-differential-privacy
List: awesome-differential-privacy
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list of differential-privacy related resources
- Host: GitHub
- URL: https://github.com/menisadi/awesome-differential-privacy
- Owner: menisadi
- License: cc0-1.0
- Created: 2016-12-04T13:05:04.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-03-28T15:57:04.000Z (9 months ago)
- Last Synced: 2024-05-19T15:00:48.552Z (7 months ago)
- Topics: algorithm, awesome, awesome-list, privacy
- Homepage:
- Size: 46.9 KB
- Stars: 272
- Watchers: 9
- Forks: 44
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ppml - awesome-differential-privacy - for differential privacy (Uncategorized / Uncategorized)
- awesome-PETs - menisadi/awesome-differential-privacy
- awesome-ppdm - awesome-differential-privacy
- ultimate-awesome - awesome-differential-privacy - List of differential-privacy related resources. (Other Lists / Monkey C Lists)
README
# Awesome Differential Privacy [](https://awesome.re)
list of differential-privacy related repositories (and a bit more)
## Contents
- [Code and Projects](#code-and-projects)
- [Learn](#learn)
- [Read](#read)
- [Industry Articles](#industry-articles)
- [YouTube](#youtube)
## Code and Projects
- [OpenDP - Developing Open Source Tools for Differential Privacy](https://opendp.org/)
- [Google's differential privacy library](https://github.com/google/differential-privacy)
- [Opacus - Training PyTorch models with differential privacy](https://github.com/pytorch/opacus)
- [Implementing TensorFlow with differential privacy](https://github.com/tensorflow/privacy)
- [Diffprivlib: The IBM Differential Privacy Library](https://github.com/IBM/differential-privacy-library)
- [JAX-Privacy - Algorithms for Privacy-Preserving Machine Learning in JAX](https://github.com/deepmind/jax_privacy)
- [Harvard University Privacy Tools Project](http://privacytools.seas.harvard.edu/)
- [Google's Private Reporting Algorithms](https://github.com/google/rappor)
- [Tumult's privacy programming framework](https://gitlab.com/tumult-labs/core)
- [PyDP - OpenMinded's python version of Google's library](https://github.com/OpenMined/PyDP)
- [PipelineDP - OpenMinded's framework for non-experts](https://github.com/OpenMined/PipelineDP)
- [MW - synthetic data](https://github.com/mrtzh/PrivateMultiplicativeWeights.jl)
- [GUPT - Data Mining Platform](https://github.com/prashmohan/GUPT)
- [Easy differential privacy in R](https://github.com/brubinstein/diffpriv)
- [Dataflow analysis & differential privacy for SQL queries](https://github.com/uber/sql-differential-privacy)
- [Differential Privacy package for WEKA](https://github.com/NidhiKat/DPWeka)
- [ARX - open source data anonymization tool](https://github.com/arx-deidentifier/arx)
- [diffpriv - Easy differential privacy in R](http://www.bipr.net/diffpriv/)
- [DP-XGBoost - Private Machine Learning at Scale](https://github.com/sarus-tech/dp-xgboost)
- [Qrlew - The SQL-to-SQL Differential Privacy layer](https://qrlew.github.io/)
## Learn
- [The Algorithmic Foundations of Differential Privacy - Book](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf)
- [Gautam Kamath - A Course In Differential Privacy](https://www.youtube.com/playlist?list=PLmd_zeMNzSvRRNpoEWkVo6QY_6rR3SHjp)
- [BIU winter school - Differential Privacy: From Theory to Practice](https://www.youtube.com/playlist?list=PL8Vt-7cSFnw1li73YXZdTaiAeXFkmWWRh)
- [CS 860 - Algorithms for Private Data Analysis - Fall 2020](http://www.gautamkamath.com/CS860-fa2020.html)
- [Programming Differential Privacy - Online book about differential privacy, for programmers](https://programming-dp.com/)
- [Towards Data Science - Differential Privacy in Deep Learning](https://towardsdatascience.com/differential-privacy-in-deep-learning-cf9cc3591d28)
## Read
- [Wikipedia](https://en.wikipedia.org/wiki/Differential_privacy)
- [A Primer for a Non-technical Audience](http://privacytools.seas.harvard.edu/files/privacytools/files/pedagogical-document-dp_0.pdf)
- [Differential privacy for dummies](https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md)
- [Introducing TensorFlow Privacy](https://blog.tensorflow.org/2019/03/introducing-tensorflow-privacy-learning.html)
- [Introducing Opacus](https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy/)
- [An illustrated primer](https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-06.md)
- [Win-Vector Blog](http://www.win-vector.com/blog/2015/11/our-differential-privacy-mini-series/)
- [The Promise of Differential Privacy - A Tutorial on Algorithmic Techniques](https://www.microsoft.com/en-us/research/wp-content/uploads/2011/10/PID2016981.pdf)
- [Visualizing the accuracy-privacy trade-off - Demo](https://priyakalot.github.io/ViP-demo/)
- [Laplacian Noisy Counting mechanism illustratioin](https://georgianpartners.shinyapps.io/interactive_counting/)
## Industry Articles
- [Apple - Learning with Privacy at Scale](https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html)
- [Apple - Differential Privacy](https://www.apple.com/privacy/docs/Differential_Privacy_Overview.pdf)
- [Google - Deep Learning with Differential Privacy](https://ai.google/research/pubs/pub45428)
- [Microsoft - Privacy and accuracy: How Cynthia Dwork is making data analysis better](https://blogs.microsoft.com/ai/privacy-and-accuracy-how-cynthia-dwork-is-making-data-analysis-better/)
- [Microsoft - Collecting telemetry data privately](https://www.microsoft.com/en-us/research/blog/collecting-telemetry-data-privately/)
- [How One of Apple's Key Privacy Safeguards Falls Short](https://www.wired.com/story/apple-differential-privacy-shortcomings/)
- [Googles Differential Privacy May be Better Than Apples](https://www.macobserver.com/analysis/google-apple-differential-privacy/)
- [Privacy and machine learning: two unexpected allies?](http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html)
## YouTube
- [The Definition of Differential Privacy - Cynthia Dwork](https://www.youtube.com/watch?v=lg-VhHlztqo)
- [Tutorial on Differential Privacy](https://www.youtube.com/watch?v=ekIL65D0R3o)
- [A Practical Beginners' Guide to Differential Privacy](https://www.youtube.com/watch?v=Gx13lgEudtU)
- [Accessing Data while Preserving Privacy](https://www.youtube.com/watch?v=GBw07xJyc0c&t=20s)
- [Minutephysics - Protecting Privacy with MATH (Collab with the Census)](https://www.youtube.com/watch?v=pT19VwBAqKA)