The US Census Bureau plans to protect the privacy of 2020 Census respondents through its Disclosure Avoidance System (DAS), which attempts to achieve differential privacy guarantees by adding noise to the Census microdata. By applying redistricting simulation and analysis methods to DAS-protected 2010 Census data, we find that the protected data are not of sufficient quality for redistricting purposes. We demonstrate that the injected noise makes it impossible for states to accurately comply with the One Person, One Vote principle. Our analysis finds that the DAS-protected data are biased against certain areas, depending on voter turnout and partisan and racial composition, and that these biases lead to large and unpredictable errors in the analysis of partisan and racial gerrymanders. Finally, we show that the DAS algorithm does not universally protect respondent privacy. Based on the names and addresses of registered voters, we are able to predict their race as accurately using the DAS-protected data as when using the 2010 Census data. Despite this, the DAS-protected data can still inaccurately estimate the number of majority-minority districts. We conclude with recommendations for how the Census Bureau should proceed with privacy protection for the 2020 Census.

翻译：美国人口普查局计划通过其披露避免系统(DAS)保护2020年人口普查答卷人的隐私,该系统试图通过在人口普查微观数据中增加噪音来实现不同的隐私保障。我们发现,通过对受DAS保护的2010年人口普查数据采用重新分区模拟和分析方法,受保护数据的质量不足以重新划分;我们证明,注射噪音使各州无法准确地遵守“一人一票”原则;我们的分析发现,受DAS保护的数据偏向于某些地区,取决于选民投票率以及党派和种族组成,这些偏向导致在分析党派和种族格里曼德人时出现大量和不可预测的错误。最后,我们表明DAS算法并不普遍保护被调查者的隐私。根据登记选民的姓名和地址,我们能够预测他们的种族,在使用2010年人口普查数据时,使用受DAS保护的数据是准确的。尽管如此,受DAS保护的数据仍然可以不准确地估计多数选区和少数民族区的数目。我们最后建议人口普查局如何着手保护2020年人口普查的隐私。