Abstract: The US Census collects large quantities of data that can be useful for research and decision making by policymakers, businesses, and academics. As much of the collected data pertain to individuals, households, and establishments, the US Census also has a legal obligation to protect their privacy and hence has long employed statistical disclosure limitation (SDL) techniques to guarantee the confidentiality of the collected data while releasing data and analysis results to the public. However, the SDL techniques traditionally make it difficult if not impossible to determine with confidence that these two requirements - privacy and utility - have been satisfied. In this project - Formal Privacy Models and Title 13 - computer scientists and legal scholars seek to further the use of formal privacy models such as differential privacy with data that is collected, analyzed and disseminated by the Census Bureau. The project aims to address two major challenges: (a) Develop an understanding on how to bridge the wide conceptual and practical gap between the approaches found in formal privacy models and the heuristic approaches in current use and contemplated by existing regulatory and policy frameworks; and (b) Close gaps between theoretical developments showing that formal privacy models like differential privacy permit, in principle and the actual use of analysis and publication techniques by the BOC.
Bio: Kobbi Nissim is McDevitt Chair in Computer Science, Georgetown University. Nissim’s work is focused on the mathematical formulation and understanding of privacy. His work from 2003 and 2004 with Dinur and Dwork initiated rigorous foundational research of privacy and presented a precursor of Differential Privacy - a definition of privacy in computation that he introduced in 2006 with Dwork, McSherry and Smith. His research studies privacy in various contexts, including statistics, computational learning, mechanism design, social networks, and more recently law and policy. Since 2011, Nissim has been involved with the Privacy Tools for Sharing Research Data project at Harvard, developing privacy-preserving tools for the sharing of social-science data. Nissim was awarded the Godel Prize In 2017, the IACR TCC Test of Time Award in 2016, and the ACM PODS Alberto O Mendelzon Test-of-Time Award in 2013.