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.
Long-Term Participants (tentative list, including organizers):
Raef Bassily (Ohio State University), Cynthia Dwork (Harvard University), Vitaly Feldman (Google), Will Fithian (UC Berkeley), Marco Gaboardi (SUNY Buffalo), Moritz Hardt (UC Berkeley), Chris Hoofnagle (UC Berkeley), Frauke Kreuter (University of Maryland), Katrina Ligett (Hebrew University), Deirdre Mulligan (UC Berkeley), Moni Naor (Weizmann Institute), Sasho Nikolov (University of Toronto), Helen Nissenbaum (Cornell Tech), Kobbi Nissim (Georgetown University), Sofya Raskhodnikova (Boston University), Guy Rothblum (Weizmann Institute), Anand Sarwate (Rutgers University), Or Sheffet (University of Alberta), Aleksandra Slavkovic (Pennsylvania State University), Adam Smith (Boston University), Jonathan Ullman (Northeastern University), Alexandra Wood (Harvard University), Steven Wu (University of Minnesota Twin Cities).
Mark Bun (Princeton University), Rachel Cummings (Georgia Insitute of Technology), Gautam Kamath (Massachusetts Institute of Technology), Vishesh Karwa (Ohio State University), Aleksandra Korolova (University of Southern California), Audra McMillan (University of Michigan), Matthew Reimherr (Pennsylvania State University), Thomas Steinke (IBM Almaden).
Visiting Graduate Students and Postdocs:
Yahav Bechavod (Hebrew University), Albert Cheu (Northeastern University), Samantha Chiu (University of Maryland), Alex Edmonds (University of Toronto), Jake Goldenfein (Cornell Tech), Aaron Koolyk (Hebrew University), Mason Marks (Cornell Tech), Anupama Nandi (Ohio State University), Marcel Neunhoeffer (University of Mannheim), Elizabeth O’Neill (Cornell Tech), Gian Pietro Farina (University at Buffalo, SUNY), Moshe Shenfeld (Hebrew University), Vikrant Singhal (Northeastern University), Ramesh Krishnan Pallavoor Suresh (Boston University), Om Dipakbhai Thakkar (Boston University), Neil Vexler (Weizmann Institute of Science), Di Wang (University at Buffalo, SUNY), Yvonne Wang (Cornell Tech), Ilias Zadik (MIT), Juba Ziani (Caltech)
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).