Spring 2019

Data Privacy: Foundations and Applications

Jan. 15May 17, 2019

This program aims to advance core research on privacy and to foster new collaborations between researchers who work on theoretical aspects of data privacy and those working in areas of potential applications.

A rigorous foundational approach to private data analysis has emerged in theoretical computer science in the last decade, with differential privacy and its close variants playing a central role. This approach was motivated by the vast amounts of personal information that are collected and analyzed by a wide range of institutions, and by numerous demonstrated failures of traditional statistical disclosure limitation paradigms, most notably de-identification.  The resulting body of theoretical work draws on many scientific fields: statistics, machine learning, cryptography, algorithms, databases, information theory, economics and game theory.

This research has begun to influence how privacy scholars think and argue about privacy, and how sensitive data are treated in real life applications.  This ongoing process reinforces the need for further technical research on foundations of privacy and understanding of the technical, legal, social and ethical issues that arise. The program will bring together experts from across disciplines related to privacy: theoretical computer science, statistics, game theory, machine learning, databases, social science and law to share their knowledge, strengthen the connections between privacy-related research in these disciplines and work on a wide-reaching interdisciplinary agenda involving technology and policy.  The participants will investigate basic privacy-related algorithmic, statistical, and complexity- and game-theoretic questions; study barriers to implementing and using the products of theoretical research in privacy; and explore methodologies for bridging the gaps between mathematical approaches to privacy and approaches rooted in the law, social norms and ethics.

Organizers: Vitaly Feldman (IBM Almaden), Katrina Ligett (Hebrew University, Jerusalem and Caltech), Kobbi Nissim (Georgetown University), Adam Smith (Pennsylvania State University).

Long-Term Participants (tentative list, including organizers):
Avrim Blum (Carnegie Mellon University), Kamalika Chaudhuri (UC San Diego), Cynthia Dwork (Harvard University), Vitaly Feldman (IBM Research), Will Fithian (UC Berkeley), Marco Gaboardi (SUNY Buffalo), Frauke Kreuter (University of Maryland), Jing Lei (Carnegie Mellon University), Katrina Ligett (Caltech & Hebrew University, Jerusalem), Frank McSherry, Deirdre Mulligan (UC Berkeley), Moni Naor (Weizmann Institute), Alexandr Nikolov (University of Toronto), Kobbi Nissim (Georgetown University), Sofya Raskhodnikova (Pennsylvania State University), Omer Reingold (Stanford University), Aaron Roth (University of Pennsylvania), Guy Rothblum (Weizmann Institute), Tim Roughgarden (Stanford University), Aleksandra Slavkovic (Pennsylvania State University), Adam Smith (Pennsylvania State University), Kunal Talwar (Google), Jon Ullman (Northeastern University), Salil Vadhan (Harvard University), Martin Wainwright (UC Berkeley).




Vitaly Feldman (Google), Katrina Ligett (Hebrew University and Caltech), Kobbi Nissim (Georgetown University), Adam Smith (Pennsylvania State University)


Marco Gaboardi (SUNY Buffalo), Ashwin Machanavajjhala (Duke University), Kobbi Nissim (Georgetown University), Natalie Shlomo (University of Manchester)


Kamalika Chaudhuri (UC San Diego), Aleksandra Slavkovic (Pennsylvania State University), Adam Smith (Pennsylvania State University), Martin Wainwright (UC Berkeley)


Katrina Ligett (Hebrew University and Caltech), Kobbi Nissim (Georgetown University), Aaron Roth (University of Pennsylvania), Salil Vadhan (Harvard University)

Those interested in participating in this program should send email to the organizers privacy2019 [at] lists [dot] simons [dot] berkeley [dot] edu (at this address).