Understanding and Mitigating the Impacts of Differentially Private Census Data on State Level Redistricting
<|reference_start|>Understanding and Mitigating the Impacts of Differentially Private Census Data on State Level Redistricting: Data from the Decennial Census is published only after applying a disclosure avoidance system (DAS). Data users were shaken by the adoption of differential privacy in the 2020 DAS, a radical departure from past methods. The change raises the question of whether redistricting law permits, forbids, or requires taking account of the effect of disclosure avoidance. Such uncertainty creates legal risks for redistricters, as Alabama argued in a lawsuit seeking to prevent the 2020 DAS's deployment. We consider two redistricting settings in which a data user might be concerned about the impacts of privacy preserving noise: drawing equal population districts and litigating voting rights cases. What discrepancies arise if the user does nothing to account for disclosure avoidance? How might the user adapt her analyses to mitigate those discrepancies? We study these questions by comparing the official 2010 Redistricting Data to the 2010 Demonstration Data -- created using the 2020 DAS -- in an analysis of millions of algorithmically generated state legislative redistricting plans. In both settings, we observe that an analyst may come to incorrect conclusions if they do not account for noise. With minor adaptations, though, the underlying policy goals remain achievable: tweaking selection criteria enables a redistricter to draw balanced plans, and illustrative plans can still be used as evidence of the maximum number of majority-minority districts that are possible in a geography. At least for state legislatures, Alabama's claim that differential privacy ``inhibits a State's right to draw fair lines'' appears unfounded.<|reference_end|>
@article{cianfarani2024understanding,
    title={Understanding and Mitigating the Impacts of Differentially Private
  Census Data on State Level Redistricting},
    author={Christian Cianfarani and Aloni Cohen},
    journal={arXiv preprint arXiv:2409.06801},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06801},
    primaryClass={cs.CY}
}