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

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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.