ACM Paris Kanellakis Theory and Practice Award
USA - 2021
For the formulation and development of the theory of differential privacy and its application to preserving privacy in statistical databases
The theory of differential privacy gives a conceptually new, simple, and mathematically rigorous definition of privacy that allows for the development of mechanisms which provably protect individual information while at the same time enabling statistical analyses of databases. A sequence of papers by Avrim Blum, Irit Dinur, Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith introduced this theory and showed how it could be extended to a wide range of applications.
Differential privacy has important advantages over previously used methods: it requires no assumptions about the knowledge of the attacker and its computational capabilities; and it allows for formal analysis of privacy under composition. It is relatively easy to explain and use and has broad applicability. Differential privacy has been employed by a number of large companies and start-ups and notably the 2020 US Census, to make available data for analyses, including machine learning, with applications from marketing to social science research.