Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity

TL;DR

This paper proposes two differentially private algorithms for releasing detailed race and ethnicity statistics from the 2020 US Census, balancing privacy guarantees with data accuracy.

Contribution

It introduces two novel DP algorithms using Geometric and Discrete Gaussian noise for census data release, with analytical privacy loss estimates.

Findings

Algorithms satisfy pure-DP and zCDP respectively

Analytical privacy loss estimates provided

Applicable to large-scale demographic data

Abstract

This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (DHC) Race & Ethnicity tabulations as part of the 2020 Census. The tabulations contain statistics (counts) of demographic and housing characteristics of the entire population of the US crossed with detailed races and tribes at varying levels of geography. We describe two differentially private algorithmic strategies, one based on adding noise drawn from a two-sided Geometric distribution that satisfies "pure"-DP, and another based on adding noise from a Discrete Gaussian distribution that satisfied a well studied variant of differential privacy, called Zero Concentrated Differential Privacy (zCDP). We analytically estimate the privacy loss parameters ensured by the two algorithms for comparable levels of error introduced in the statistics.