论文标题
2020年的人口普查披露避免系统上下算法
The 2020 Census Disclosure Avoidance System TopDown Algorithm
论文作者
论文摘要
人口普查上下算法（TDA）是使用差异隐私用于隐私会计的披露避免系统。该算法摄入了2020年人口普查数据和最终制表地理定义的最终编辑版本。然后，该算法使用零浓缩的差分隐私在数据上创建了数据的关键查询噪声版本，称为测量。 TDA的另一个关键方面是不变的人，即人口普查局根据政策而确定的统计数据，将其排除在隐私损失会计之外。 TDA后处理将测量结果与不变式一起生成一个微数据详细信息文件（MDF），该文件包含每个人的一个记录，以及在2020年普查中列举的每个住房单元的记录。 MDF传递给2020年的人口普查制表系统，以生成2020年的人口普查重新划分数据（P.L. 94-171）摘要文件。本文为此描述了TDA的数学和测试。
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.
