Differential Privacy and the US Census | Cynthia Dwork | WiDS 2019

Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard University.

Differential privacy is a mathematically rigorous definition of privacy tailored to statistical analysis of large datasets. Differentially private systems simultaneously provide useful statistics to the well-intentioned data analyst and strong protection against arbitrarily powerful adversarial system users — without needing to distinguish between the two. Differentially private systems “don’t care” what the adversary knows, now or in the future. Finally, differentially private systems can rigorously bound and control the cumulative privacy loss that accrues over many interactions with the confidential data. These unique properties, together with the abundance of auxiliary data sources and the ease with which they can be deployed by a privacy adversary, led the US Census Bureau to adopt differential privacy as the disclosure avoidance methodology of the 2020 decennial census.

This talk will motivate the definition of differential privacy, describe some of the techniques to be used in the 2020 census, and explain some of the utility gains over previous methods and the sense in which differential privacy provides a best-possible solution. Finally, the talk will highlight a few of the many remaining challenges.