The Impact of the U.S. Census Disclosure Avoidance System on Redistricting and Voting Rights Analysis
arXiv:2105.14197 · doi:10.1126/sciadv.abk3283
Abstract
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.
42 pages, 22 figures. New postscript analyzing newly-released DAS-19.61 revision
References in corpus (1)
Cited by in corpus (9)
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- A Note on the Misinterpretation of the US Census Re-identification Attack
- Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System
- Estimating Racial Disparities When Race is Not Observed
- The Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization
- General Inferential Limits Under Differential and Pufferfish Privacy