| layout | title | permalink |
|---|---|---|
page |
Resources |
/resources/ |
- The Algorithmic Foundations of Differential Privacy, by Cynthia Dwork and Aaron Roth
- The Complexity of Differential Privacy, by Salil Vadhan
- 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
- Privacy Preserving Machine Learning, taught by Aurélien Bellet, Winter 2021
- 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
- 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
- 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
- Theory and Practice of Differential Privacy (TPDP)
- Privacy Preserving Machine Learning (PPML)
- Privacy Preserving AI (PPAI)