SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)

• URL: http://arxiv.org/abs/2505.01472v1
• Date: Fri, 02 May 2025 13:08:28 GMT
• Title: SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)
• Authors: Sam Haney, Skye Berghel, Bayard Carlson, Ryan Cumings-Menon, Luke Hartman, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Amritha Pai, Simran Rajpal, David Pujol, William Sexton, Ruchit Shrestha, Daniel Simmons-Marengo,
• Abstract summary: The article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (DHC-A) of the 2020 Census.<n>The SafeTab-P algorithm is based on adding noise drawn to statistics of interest from a discrete Gaussian distribution.<n>We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy (zCDP)
• Score: 7.787555954397617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) of the 2020 Census. The tabulations contain statistics (counts) of demographic characteristics of the entire population of the United States, crossed with detailed races and ethnicities at varying levels of geography. The article describes the SafeTab-P algorithm, which is based on adding noise drawn to statistics of interest from a discrete Gaussian distribution. A key innovation in SafeTab-P is the ability to adaptively choose how many statistics and at what granularity to release them, depending on the size of a population group. We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy (zCDP). We then describe how the algorithm was implemented on Tumult Analytics and briefly outline the parameterization and tuning of the algorithm.

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