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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

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# Awesome Differential Privacy [![Awesome](https://awesome.re/badge.svg)](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)