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


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.

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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)


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
  • Census
  • TopDown
  • differential privacy
  • redistricting
  • Voting Rights Act


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