https://www.science.org/doi/10.1126/sciadv.abk3283
Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureauβs latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral districts. We find that the DAS systematically undercounts the population in mixed-race and mixed-partisan precincts, yielding unpredictable racial and partisan biases. While the DAS leads to a likely violation of the βOne Person, One Voteβ standard as currently interpreted, it does not prevent accurate predictions of an individualβs race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census.
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