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Census TopDown: The Impacts of Differential Privacy on Redistricting

Authors Aloni Cohen, Moon Duchin, JN Matthews, Bhushan Suwal

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Author Details

Aloni Cohen
  • Hariri Institute for Computing and School of Law, Boston University, MA, USA
Moon Duchin
  • Department of Mathematics, Tufts University, Medford, MA, USA
JN Matthews
  • Tisch College of Civic Life, Tufts University, Medford, MA, USA
Bhushan Suwal
  • Tisch College of Civic Life, Tufts University, Medford, MA, USA

Funding

This project was supported on NSF OIA-1937095 (Convergence Accelerator) and by a grant from the Alfred P. Sloan Foundation.
  • Cohen, Aloni: NSF CNS-1414119; NSF CNS-1915763; DARPA HR00112020021.
  • Duchin, Moon: NSF DMS-2005512

Acknowledgements

Authors are listed alphabetically. We thank Denis Kazakov, Mark Hansen, and Peter Wayner. Kazakov developed the reconstruction algorithm as a member of Hansen’s research group. Wayner guided our deployment of TopDown in AWS and was an invaluable team member for the technical report. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of our funders.

Cite AsGet BibTex

Aloni Cohen, Moon Duchin, JN Matthews, and Bhushan Suwal. Census TopDown: The Impacts of Differential Privacy on Redistricting. In 2nd Symposium on Foundations of Responsible Computing (FORC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 192, pp. 5:1-5:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.FORC.2021.5

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.

Subject Classification

ACM Subject Classification
  • Security and privacy
  • Applied computing → Law
  • Applied computing → Voting / election technologies
Keywords
  • Census
  • TopDown
  • differential privacy
  • redistricting
  • Voting Rights Act

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Supplementary Materials

References

  1. 13 U.S.C. Section 9. URL: https://www.law.cornell.edu/uscode/text/13/9.
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  16. National Conference of State Legislatures. 2010 Redistricting Deviation Table. URL: https://www.ncsl.org/research/redistricting/2010-ncsl-redistricting-deviation-table.aspx.
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