The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The algorithm then creates noisy versions of key queries on the data, referred to as measurements, using zero-Concentrated Differential Privacy. Another key aspect of the TDA are invariants, statistics that the Census Bureau has determined, as matter of policy, to exclude from the privacy-loss accounting. The TDA post-processes the measurements together with the invariants to produce a Microdata Detail File (MDF) that contains one record for each person and one record for each housing unit enumerated in the 2020 Census. The MDF is passed to the 2020 Census tabulation system to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File. This paper describes the mathematics and testing of the TDA for this purpose.
翻译:普查“顶层数据”(TDA)是一种避免披露制度,它使用隐私损失核算方面的不同隐私。算法记录了2020年人口普查数据的最后编辑版本和最后列表地理定义。算法随后制造了数据的关键查询的噪音版本,称为测量,使用零集中的差别隐私。普查“顶层数据”的另一个关键方面是变量,普查局决定,在政策上将统计数据排除在隐私损失核算之外。TDA后处理测量数据,与变量一起制作一个微数据详细文件(MDF),其中每个个人都有一份记录,2020年人口普查所列举的每个住房单元都有一份记录。MDF被转到2020年普查制表系统,以生成2020年人口普查重新划分数据(P.L.94-171)简要文件。本文描述了数字和为此对TDA的测试。