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Differential Privacy: Theoretical and Practical Challenges

Salil Vadhan

Salil Vadhan
Harvard University


Thursday, January 15, 2015
Noon-1pm,
NSF Stafford I, Room 110

To watch the recorded presentation, register at register for the free webcast.

Abstract:

Differential Privacy is framework for enabling the analysis of privacy-sensitive datasets while ensuring that individual-specific information is not revealed. The concept was developed in a body of work in theoretical computer science starting about a decade ago. It is now flourishing as an area of research, with deep connections to many other topics in theory. At the same time, its potential for addressing pressing privacy problems in a variety of domains has attracted the interest of scholars from many other areas, including statistics, databases, medical informatics, law, social science, computer security and programming languages.

In this talk, I will give a general introduction to differential privacy, and discuss some of the theoretical and practical challenges for future work in this area. I will also describe a large, multidisciplinary research project at Harvard, called "Privacy Tools for Sharing Research Data," in which we are working on some of these challenges as well as others associated with the collection, analysis, and sharing of personal data for research in social science and other fields.

Speaker:

Salil Vadhan is the Vicky Joseph Professor of Computer Science and Applied Mathematics in the Harvard University School of Engineering and Applied Sciences, and the Director of the Harvard Center for Research on Computation and Society. He received his PhD in Applied Mathematics from MIT in 1999, and was an NSF Postdoctoral Fellow at MIT and the Institute for Advanced Study before joining the Harvard faculty in 2001. He is a recipient of a Simons Investigator Award, a Godel Prize, a Guggenheim Fellowship, a Phi Beta Kappa Award for Excellence in Teaching, and the ACM Doctoral Dissertation Award.

Vadhan's research area is theoretical computer science, specifically computational complexity, cryptography, and differential privacy. He is the Lead PI on an NSF frontier project "Privacy Tools for Sharing Research Data" (http://privacytools.seas.harvard.edu/). This is a broad, multidisciplinary effort at Harvard to help enable the collection, analysis, and sharing of personal data for research in social science and other fields while providing privacy for the data subjects. Bringing together computer science, social science, statistics, and law, the project seeks to advance our understanding of privacy and data utility, and design an array of technological, legal, and policy tools for dealing with sensitive data.