Differential privacy is a definition of privacy tailored to the analysis of large datasets. The key to the success of differential privacy is the ability to quantify and reason about cumulative privacy loss over many differentially private interactions. When upper bounds on privacy loss are loose, the deployment of the algorithms is by definition conservative, “leaving something on the table.” Under high levels of composition, much potential utility is lost. We survey two general approaches to getting more utility: privacy amplification techniques, which are algorithmic, and definitional changes, which lead to tighter analyses of existing algorithms.

Cynthia Dwork, Gordon McKay Professor of Computer Science at the Harvard Paulson School of Engineering, Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study, and Affiliated Faculty at Harvard Law School and Department of Statistics, uses theoretical computer science to place societal problems on a firm mathematical foundation.

She was awarded the Edsger W. Dijkstra Prize in 2007 in recognition of some of her earliest work establishing the pillars on which every fault tolerant system has been built for a generation (Dwork, Lynch, and Stockmeyer, 1984).

Her contributions to cryptography include the launching of non-malleable cryptography, the subfield of modern cryptography that studies – and remedies – the failures of cryptographic protocols to compose securely (Dolev, Dwork, and Naor, 1991). She is a co-inventor of the first public-key cryptosystem based on lattices, the current best bet for cryptographic constructions that will remain secure even against quantum computers (Ajtai and Dwork, 1997). More recently, Dwork spearheaded a successful effort to place privacy-preserving analysis of data on a firm mathematical foundation. A cornerstone of this effort is the invention of Differential Privacy (Dwork, McSherry, Nissim, and Smith, 2006, Dwork 2006), now the subject of intense activity across many disciplines and recipient of the Theory of Cryptography Conference 2016 Test-of-Time award and the 2016 Gödel Prize. Now widely used in industry – for example by Google, MIcrosoft, Uber, and, most prominently, by Apple – differential privacy will also be the foundation of the Disclosure Avoidance System in the 2020 US Decennial Census.

Dwork was educated at Princeton and Cornell. She received her BSE (with honors) in electrical engineering and computer science at Princeton University, where she also received the Charles Ira Young Award for Excellence in Independent Research, the first woman ever to do so. She received her M.Sc. and Ph.D. degrees in computer science at Cornell University.

Dwork is a member of the US National Academy of Sciences and the US National Academy of Engineering, and is a fellow of the ACM, the American Academy of Arts and Sciences, and the American Philosophical Society.

Organizers: Paul H. Siegel (University of California, San Diego), Netanel Raviv, (Washington University, St. Louis), Anxiao (Andrew) Jiang (Texas A&M University)