Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity
Sam Haney, William Sexton, Ashwin Machanavajjhala, Michael, Hay, Gerome Miklau

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Census and Population Estimation
