Computer Science Building, Room 151
Faculty Host: Gerome Miklau
The problem of statistical disclosure control - revealing accurate statistics about a population while preserving the privacy of individuals - has a venerable history. An extensive literature spans multiple disciplines: statistics, theoretical computer science, security, and databases. Yet privacy breaches abound, both on paper and in practice.
This talk describes a large body of work revisiting the problem from the perspective of modern cryptography. We define differential privacy, the first mathematically rigorous and comprehensive notion of privacy tailored to private data analysis. We then present general techniques for achieving differential privacy while simultaneously preserving utility of the data, together with impossibility results that guided its development.
Finally, we describe a few of the exciting new directions this work has recently taken.
Cynthia Dwork, a theoretical computer scientist, has made fundamental contributions to cryptography, distributed computing, and complexity theory. Her current focus is the development of a mathematically rigorous framework and algorithmic techniques for the privacy-preserving analysis of data. A Distinguished Scientist at Microsoft, Dwork is a recipient of the Edsger W. Dijkstra Prize and a member of the US National Academy of Engineering and the American Academy of Arts and Sciences.
A reception will be held at 3:40 PM in the atrium, outside the presentation room.