# The Impact of the U.S. Census Disclosure Avoidance System on Redistricting and Voting Rights Analysis

arXiv: https://arxiv.org/abs/2105.14197 · HTML: https://mlsanity.com/paper/2105.14197/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2021.

## Built on by (most cited first, 19 of 19)

- [A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data](https://mlsanity.com/paper/2301.10053/) (2023)
- [Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements](https://mlsanity.com/paper/2205.06129/) (2022)
- [Differential Privacy with Random Projections and Sign Random Projections](https://mlsanity.com/paper/2306.01751/) (2023)
- [A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy](https://mlsanity.com/paper/2312.11712/) (2023)
- [The 2020 US Decennial Census is more private than you (might) think](https://mlsanity.com/paper/2410.09296/) (2024)
- [Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees](https://mlsanity.com/paper/2508.20736/) (2025)
- [Frugal, Flexible, Faithful: Causal Data Simulation via Frengression](https://mlsanity.com/paper/2508.01018/) (2025)
- [How should we proxy for race/ethnicity? Comparing Bayesian improved surname geocoding to machine learning methods](https://mlsanity.com/paper/2206.14583/) (2022)
- [Towards more accurate and useful data anonymity vulnerability measures](https://mlsanity.com/paper/2403.06595/) (2024)
- [The Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization](https://mlsanity.com/paper/2508.18976/) (2025)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata](https://mlsanity.com/paper/2311.03171/) (2023)
- [Testing Credibility of Public and Private Surveys through the Lens of Regression](https://mlsanity.com/paper/2410.05458/) (2024)
- [Optimal Survey Design for Private Mean Estimation](https://mlsanity.com/paper/2501.18121/) (2025)
- [Dimension Agnostic Testing of Survey Data Credibility through the Lens of Regression](https://mlsanity.com/paper/2508.20616/) (2025)
- [Near-Optimal Private Tests for Simple and MLR Hypotheses](https://mlsanity.com/paper/2601.21959/) (2026)
- [Bayesian Adversarial Privacy](https://mlsanity.com/paper/2603.04199/) (2026)
- [A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Census](https://mlsanity.com/paper/2606.29835/) (2026)
- [Shapley-Value-Based Feature Attribution for Data Masking](https://mlsanity.com/paper/2607.28946/) (2026)

## Abstract (arXiv)

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.