Impact of the implementation of differential privacy to decennial census data in understanding of health disparities in the United States

对十年一次的人口普查数据实施差别隐私对了解美国健康差异的影响

基本信息

批准号：
10349747
负责人：
Alexis R Santos-Lozada
金额：
$ 7.91万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2024-03-31
项目状态：
已结题

项目摘要

Summary The implementation of new differential-avoidance methods, based on differential privacy algorithms, to the 2020 U.S. Census data and additional data products may affect the usefulness of these data, the accuracy of estimates and rates derived from them, and critical knowledge about social phenomena such as health disparities. The U.S. Census has released a series of demonstration products that allow comparisons between the counts produced under traditional and the proposed privacy algorithm. We test the ramifications of differential privacy by studying changes in the county-level age-specific mortality rates (ASMRs) for the overall population and racial/ethnic groups. We ask how changes in the denominators, due to the implementation of differential privacy, result in different ASMRs. We further propose the examination of trends by comparing the conclusions derived from 2000-2010 comparisons of ASMRs using mortality rates produced using both sets of denominators. Further, we propose quantifying the changes produced by the implementation of differential privacy in the empirical associations derived from regression models. We address these aims by examining mortality rates produced using different vintages of the 2010 decennial counts produced using differential privacy and comparing them with those produced using the traditional methods. These findings will highlight the consequences of implementing differential privacy for research examining changes in rates produced by artificial population composition changes. Overall, we will demonstrate the challenges of using the data products derived from the proposed disclosure avoidance method while highlighting critical instances where scientific understandings may be negatively impacted.

概括基于差异隐私算法的新的差异避免方法的实施人口普查数据和其他数据产品可能会影响这些数据的有用性，估计和速率的准确性从他们那里，以及关于健康差异等社会现象的批判知识。美国人口普查发布了系列示范产品，允许在传统和拟议的隐私方面进行比较算法。我们通过研究县级特定年龄的死亡率的变化来测试差异隐私的后果总人口和种族/族裔的利率（ASMR）。我们询问分母的变化是如何的实施差异隐私，导致不同的ASMR。我们进一步提出了对趋势的研究比较使用两组产生的死亡率从2000-2010比较中得出的结论分母。此外，我们建议量化通过在从回归模型得出的经验关联。我们通过检查使用使用的死亡率来解决这些目标使用差异隐私产生的2010年十年型次数的不同年份，并将其与生产的年份进行比较使用传统方法。这些发现将突出实施差异隐私的后果研究检查人造种群组成所产生的速率变化的研究。总的来说，我们将证明使用从提议的避免避免方法中得出的数据产品的挑战，同时突出关键科学理解可能会受到负面影响的实例。