Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity
This addresses privacy concerns for the US Census Bureau and the public in handling sensitive demographic data, but it is incremental as it applies existing differential privacy methods to a specific dataset.
The paper tackles the problem of releasing detailed race and ethnicity tabulations from the 2020 US Census while ensuring privacy, proposing two differentially private algorithms (Geometric and Discrete Gaussian) to add noise to the statistics.
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.