|
|
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. |
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. |
McCartan, Cory, Tyler Simko, and Kosuke
Imai. (2023). ``Making
Differential Privacy Work for Census Data Users.''
Harvard Data Science Review, Vol. 5, No. 4 (Fall).
|
Kenny, Christopher T., Shiro Kuriwaki, Cory
McCartan, Evan Rosenman, Tyler Simko, and Kosuke
Imai. (2023). ``Comment: The
Essential Role of Policy Evaluation for the 2020 Census Disclosure
Avoidance System..'' Harvard Data Science
Review, Special Issue 2: Dierential Privacy for the 2020
U.S. Census (January), pp. 1-16. |
Kenny, Christopher, Cory McCartan, Tyler
Simko, and Kosuke Imai. (2024). ``Census officials must
constructively engage with independent evaluations.''
Proceedings of the National Academy of Sciences (Letter),
Vol. 121, No. 11, e2321196121. |
Kenny, Christopher, Cory McCartan, Shiro
Kuriwaki, Tyler Simko, and Kosuke Imai. (2024). ``Evaluating Bias and Noise Induced by the
U.S. Census Bureau's Privacy Protection Methods.''
Science Advances, Vol 10, No. 18 (May),
pp. 1-13. |
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. (2023). ``Sequential
Monte Carlo for Sampling Balanced and Compact Redistricting
Plans.'' Annals of Applied Statistics,
Vol. 17, No. 4 (December), pp. 3300-3323. |
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.
|