The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census

TLDR

Census statistics are essential for policy and research, but to protect individual privacy agencies are adopting differential privacy, which requires postprocessing to produce usable data. We study the impact of the Census Bureau’s latest disclosure avoidance system on the redrawing of electoral districts. We analyze how the system’s noise injection and postprocessing affect districting. The system undercounts mixed‑race and mixed‑partisan precincts, creating unpredictable racial and partisan biases that threaten the One Person One Vote standard, yet it still permits accurate individual race and ethnicity predictions, highlighting the challenge of balancing accuracy and privacy.

Abstract

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 Bureau’s 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 “One Person, One Vote” standard as currently interpreted, it does not prevent accurate predictions of an individual’s race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census.