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.

中文翻译：

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用于 2020 年人口普查详细 DHC 种族和种族的差异私有算法

本文介绍了美国人口普查局正在考虑发布详细的人口和住房特征 (DHC) 种族和族裔表格的提议的差异私有 (DP) 算法，作为 2020 年人口普查的一部分。这些表格包含美国整个人口的人口统计和住房特征的统计数据（计数），以及不同地理级别的详细种族和部落。我们描述了两种不同的私有算法策略，一种基于添加从满足“纯”-DP 的双边几何分布中提取的噪声，另一种基于添加来自满足充分研究的差分隐私变体的离散高斯分布的噪声，称为零集中差分隐私（zCDP）。