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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2021.5
URN: urn:nbn:de:0030-drops-138736
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/13873/
Cohen, Aloni ;
Duchin, Moon ;
Matthews, JN ;
Suwal, Bhushan
Census TopDown: The Impacts of Differential Privacy on Redistricting
Abstract
The 2020 Decennial Census will be released with a new disclosure avoidance system in place, putting differential privacy in the spotlight for a wide range of data users. We consider several key applications of Census data in redistricting, developing tools and demonstrations for practitioners who are concerned about the impacts of this new noising algorithm called TopDown. Based on a close look at reconstructed Texas data, we find reassuring evidence that TopDown will not threaten the ability to produce districts with tolerable population balance or to detect signals of racial polarization for Voting Rights Act enforcement.
BibTeX - Entry
@InProceedings{cohen_et_al:LIPIcs.FORC.2021.5,
author = {Cohen, Aloni and Duchin, Moon and Matthews, JN and Suwal, Bhushan},
title = {{Census TopDown: The Impacts of Differential Privacy on Redistricting}},
booktitle = {2nd Symposium on Foundations of Responsible Computing (FORC 2021)},
pages = {5:1--5:22},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-187-0},
ISSN = {1868-8969},
year = {2021},
volume = {192},
editor = {Ligett, Katrina and Gupta, Swati},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2021/13873},
URN = {urn:nbn:de:0030-drops-138736},
doi = {10.4230/LIPIcs.FORC.2021.5},
annote = {Keywords: Census, TopDown, differential privacy, redistricting, Voting Rights Act}
}
