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

• URL: http://arxiv.org/abs/2505.03072v1
• Date: Fri, 02 May 2025 13:15:14 GMT
• Title: SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)
• Authors: William Sexton, Skye Berghel, Bayard Carlson, Sam Haney, Luke Hartman, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Amritha Pai, Simran Rajpal, David Pujol, Ruchit Shrestha, Daniel Simmons-Marengo,
• Abstract summary: We describe SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B.<n>We show that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy.
• Score: 7.7544849165583525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detailed DHC-B) as part of the 2020 Census. The tabulations contain household statistics about household type and tenure iterated by the householder's detailed race, ethnicity, or American Indian and Alaska Native tribe and village at varying levels of geography. We describe the algorithmic strategy which is based on adding noise from a discrete Gaussian distribution and show that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We discuss how the implementation of the SafeTab-H codebase relies on the Tumult Analytics privacy library. We also describe the theoretical expected error properties of the algorithm and explore various aspects of its parameter tuning.

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