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

TL;DR

SafeTab-H is a novel disclosure avoidance algorithm for the 2020 Census that uses discrete Gaussian noise and satisfies zero-concentrated differential privacy, ensuring privacy while maintaining data utility.

Contribution

The paper introduces SafeTab-H, a new privacy-preserving algorithm for census data that leverages discrete Gaussian noise and zero-concentrated differential privacy.

Findings

Algorithm satisfies zero-concentrated differential privacy

Theoretical analysis of expected error properties

Implementation relies on Tumult Analytics privacy library

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.