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

TL;DR

The paper presents SafeTab-P, a differential privacy algorithm for the 2020 Census data that adaptively adds noise to protect individual privacy while maintaining data utility.

Contribution

It introduces SafeTab-P, a novel privacy-preserving algorithm that adaptively determines noise addition based on population group sizes, ensuring differential privacy for census data.

Findings

SafeTab-P satisfies zero-concentrated differential privacy.

The algorithm effectively balances privacy and data utility.

Implementation details and parameter tuning are provided.

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.