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.

翻译：本文描述了美国人口普查局正在考虑作为2020年人口普查的一部分发布《详细人口和住房特征(DHC)种族和族裔列表》的拟议差别私人算法(DP),表格包含美国全部人口的人口和住房特征统计数据(数字),跨越了不同的地理层次的种族和部落。我们描述了两种不同的私人算法战略,一种是增加来自满足“纯度”-DP的两面几何分布的噪音,另一种是增加来自不同Gaussian分布的噪音,这种分布满足了不同隐私(称为Zero Concentral differental Privacies (ZCDP) ) 的精心研究的变式。 我们分析了两种算法所确保的隐私损失参数,以统计中引入的类似错误程度为依据。