Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan Rosenman, Tyler Simko, and Kosuke Imai. (2021).``The Use of Differential Privacy for Census Data and its Impact on Redistricting: The Case of the 2020 U.S. Census.'' Science Advances, Vol. 7, No. 7 (October), pp. 1-17.



The US Census Bureau plans to protect the privacy of 2020 Census respondents through its Disclosure Avoidance System (DAS), which attempts to achieve differential privacy guarantees by adding noise to the Census microdata. By applying redistricting simulation and analysis methods to DAS-protected 2010 Census data, we find that the protected data are not of sufficient quality for redistricting purposes. We demonstrate that the injected noise makes it impossible for states to accurately comply with the One Person, One Vote principle. Our analysis finds that the DAS-protected data are biased against certain areas, depending on voter turnout and partisan and racial composition, and that these biases lead to large and unpredictable errors in the analysis of partisan and racial gerrymanders. Finally, we show that the DAS algorithm does not universally protect respondent privacy. Based on the names and addresses of registered voters, we are able to predict their race as accurately using the DAS-protected data as when using the 2010 Census data. Despite this, the DAS-protected data can still inaccurately estimate the number of majority-minority districts. We conclude with recommendations for how the Census Bureau should proceed with privacy protection for the 2020 Census.

  Related Papers and Software

The previous version of this paper is available as, Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan Rosenman, Tyler Simko, and Kosuke Imai. ``The Impact of the U.S. Census Disclosure Avoidance System on Redistricting and Voting Rights Analysis.
Imai, Kosuke and Kabir Khanna. (2016). ``Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.'' Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272.
Fifield, Benjamin, Michael Higgins, Kosuke Imai, and Alexander Tarr. (2020). ``Automated Redistricting Simulator Using Markov Chain Monte Carlo.'' Journal of Computational and Graphical Statistics, Vol. 29, No. 4, pp. 715-728.
Fifield, Benjamin, Kosuke Imai, Jun Kawahara, and Christopher T. Kenny. (2020). ``The Essential Role of Empirical Validation in Legislative Redistricting Simulation.'' Statistics and Public Policy, Vol. 7, No. 1, pp 52-68.
McCartan, Cory, and Kosuke Imai. ``Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans.''
Fifield, Benjamin, Christopher T. Kenny, Cory MaCartan, and Kosuke Imai. ``redist: Computational Algorithms for Redistricting Simulation.'' available through The Comprehensive R Archive Network and GitHub.

© Kosuke Imai
 Last modified: Thu Oct 7 06:47:44 EDT 2021