Christopher Kenny presents "The Use of Differential Privacy for Census Data and its Impact on Redistricting: The Case of the 2020 U.S. Census"

Presentation Date: 

Wednesday, September 15, 2021


CGIS Knafel Building (K354) - 12:10-1:30 pm
Census statistics play a key role in public policy decisions and social science research. Yet 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 post-processed after noise injection to be usable. We study the impact of the US 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.

This is joint work with Shiro Kuriwaki, Cory McCartan, Evan T.R. Rosenman, Tyler Simko, and Kosuke Imai.
See also: 2021