Cynthia Dwork
Harvard University
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Primary Section: 34, Computer and Information Sciences Membership Type:
Member
(elected 2014)
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Biosketch
Cynthia Dwork, a Distinguished Scientist at Microsoft Research, spearheaded a successful effort to place privacy-preserving analysis of data on a firm mathematical foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. Previous positions include research staff member at IBM Almaden Research Center (1985-2000) where Dwork and her colleagues launched non-malleable cryptography, the subfield of modern cryptography that studies—and remedies—the failures of cryptographic protocols to compose securely. She is a co-inventor of proofs of computational effort, a concept at the heart of a popular crypto-currency, and co-inventor of the first public-key cryptosystem whose security depends on the worst-case, rather than average-case, hardness of the underlying problem. Dwork is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work for establishing the pillars on which every fault tolerant system has been built for more than two decades. She is a member of the US National Academy of Sciences, and the US National Academy of Engineering, and a fellow of the American Academy of Arts and Sciences. She is a founding editor of the Journal of Privacy and Confidentiality. Dr. Dwork received her BSE in Electrical Engineering and Computer Science from Princeton University and her PhD in computer science from Cornell University.
Research Interests
Dwork studies privacy-preserving data analysis, from the theoretical limits on what is feasible to the development of differentially private algorithms, as well as the regulatory and policy frameworks for adoption of privacy-preserving technology. In addition, Dwork is currently engaged in two new directions of research: defining and ensuring fairness in classification, and the development of a general technique for ensuring the validity of statistical analyses under adaptivity, that is, when the questions asked depend on the data themselves. More broadly, her research agenda is to frame real-life societal problems in a rigorous way and lay down a mathematically rigorous theoretical groundwork for addressing them; to continue to invest effort in ongoing efforts such as privacy and fairness; and to inform the policy and regulatory debate on relevant issues from a position of strong technical understanding.
