Resources

DP.org

• Check out differentialprivacy.org. Much of the resources below are taken from their resources page.

Related Courses

• Algorithms for Private Data Analysis, taught by Gautam Kamath, Fall 2020
• Private Systems, taught by Roxana Geambasu, Spring 2020
• Algorithms in Society, taught by Adam Smith, Spring 2020
• Privacy in the World of Big Data, taught by Aleksandra Korolova, Fall 2019
• Algorithms for Private Data Analysis, taught by Aleksandar Nikolov, Fall 2019
• Applied Privacy for Data Science, taught by James Honaker and Salil Vadhan, Spring 2019
• The Algorithmic Foundations of Adaptive Data Analysis, taught by Aaron Roth and Adam Smith, Fall 2017
• Rigorous Approaches to Data Privacy, taught by Jonathan Ullman, Spring 2017
• Design of Stable Algorithms for Privacy and Learning, taught by Ashwin Machanavajjhala
• Differential Privacy in Game Theory and Mechanism Design, taught by Aaron Roth, Spring 2014
• Mathematical Approaches to Data Privacy, taught by Salil Vadhan, Spring 2013
• Algorithmic Foundations of Data Privacy, taught by Aaron Roth, Fall 2011
• Algorithmic Challenges in Data Privacy, taught by Sofya Raskhodnikova and Adam Smith, Spring 2010

Books and General Surveys

• The Algorithmic Foundations of Differential Privacy, by Cynthia Dwork and Aaron Roth
• The Complexity of Differential Privacy, by Salil Vadhan

Focused Surveys

• Differential Privacy in the Shuffle Model: A Survey of Separations by Albert Cheu
• A Primer on Private Statistics, by Gautam Kamath and Jonathan Ullman
• Differential Privacy: A Primer for a Non-Technical Audience, by Alexandra Wood et al.
• Exposed! A Survey of Attacks on Private Data by Cynthia Dwork, Adam Smith, Thomas Steinke, and Jonathan Ullman

Videos

• The Theory and Practice of Differential Privacy, by Salil Vadhan, COLT 2020
• The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century, by John Abowd, ICML 2019
• Tutorial on Differentially Private Machine Learning, by Kamalika Chaudhuri and Anand Sarwate, ICML 2017
• Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges, by Ashwin Machanavajjhala, Xi He and Michael Hay, SIGMOD 2017

Workshops (with Videos)

• Challenges and New Approaches for Protecting Privacy in Federal Statistical Programs, June 2019 at the National Academies
• Data Privacy: Foundations and Applications, Spring 2019 at the Simons Institute for the Theory of Computing
• Mathematical Foundations of Data Privacy, May 2018 at the Banff International Research Station
• Differential Privacy: From Theory to Practice, February 2017 at Bar Ilan University

Workshops (without Videos)

• Theory and Practice of Differential Privacy (TPDP)
• Privacy Preserving Machine Learning (PPML)

Code Libraries

• Google's DP Library
• PyTorch Opacus
• RAPPOR
• OpenDP
• TensorFlow Privacy
• IBM Differential Privacy Library
• Discrete Gaussian
• OpenMined
• Ektelo

Tools

• DPComp