Academia, Government, & Industry in the 2020 Disclosure Avoidance System

          @misc{ scivideos_22926,
            doi = {},
            url = {https://old.simons.berkeley.edu/talks/academia-government-industry-2020-disclosure-avoidance-system},
            author = {},
            keywords = {},
            language = {en},
            title = {Academia, Government, \& Industry in the 2020 Disclosure Avoidance System},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {nov},
            note = {22926 see, \url{https://scivideos.org/simons-institute/22926}}
          }
          

Abstract

The U.S. Census Bureau adopted formally private methods to protect the principal products released based on the 2020 Decennial Census of Population and Housing. These include the Public Law 94-171 Redistricting Data Summary File (already released), the Demographic and Housing Characteristics File (DHC; in its final phase of privacy budget tuning), as well as the Detailed Demographic and Housing Characteristics File and Supplemental Demographic and Housing Characteristics File releases (in earlier phases of design, testing, and planning). Additional, smaller product releases based on the 2020 confidential data are also expected, with sub-state releases currently required to use differentially private methods. In this talk, I describe the design and a few of the major technical issues encountered in developing the TopDown algorithm (TDA), the principal formally private algorithm used to protect the PL94-171 release, and expected to be used to protect the DHC release. TDA was designed by a joint team of academic, contractor and government employees; I discuss the ways in which this collaboration worked, as well as what worked well and what was challenging, and briefly touch on the role of industry in algorithm design outside of TDA. I close with some general thoughts on ways to help form productive collaborations between academic, government, and industry expertise in formally private methods.