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

总结 基于差分隐私算法的新差分回避方法的实施，到2020年美国 人口普查数据和其他数据产品可能影响这些数据的有用性、估计数的准确性和得出的比率 从他们那里，以及对健康差距等社会现象的批判性知识。美国人口普查局发布了一系列 演示产品，允许对传统隐私和拟议隐私下产生的计数进行比较 算法我们通过研究县级年龄别死亡率的变化来测试差异隐私的后果 总体人群和种族/族裔群体的ASMR。我们问，由于气候变化， 不同隐私实现导致不同的ASMR。我们还建议审查趋势， 比较2000-2010年ASMR的死亡率得出的结论， 一种制冷剂。此外，我们建议量化的差异隐私的实施所产生的变化， 回归模型得出的经验关联。我们通过检查使用以下方法产生的死亡率来实现这些目标 2010年十年一次的不同年份的计数使用不同的隐私，并将它们与那些产生的比较 使用传统的方法。这些发现将突出实施差异隐私的后果， 研究人口组成人为变化所产生的比率变化。总的来说，我们将展示 使用从拟议的披露避免方法中获得的数据产品的挑战，同时强调关键的 科学理解可能受到负面影响的情况。

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.