About the EventThe 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 two of the exciting new directions this work has
recently taken. |
BiographyCynthia Dwork is a computer scientist at Microsoft Research who works on
distributed computing, cryptography and e-mail spam prevention. She received the Dijkstra Prize in 2007 for her work on consensus problems. Dr. Dwork was elected as a Fellow of the American Academy of Arts and Sciences (AAAS) in 2008, and as a member of the National Academy of Engineering in 2008. Dr. Dwork received her Ph.D. from Cornell University in 1983.
|
