Full Citation

Title: Private Numbers in Public Policy: Census, Differential Privacy, and Redistricting

Citation Type: Journal Article

Publication Year: 2022

ISBN:

ISSN:

DOI: 10.1162/99608F92.22FD8A0E

NSFID:

PMCID:

PMID:

Abstract: The 2020 Decennial Census in the United States was 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 nine localities in Texas and Arizona, we find reassuring evidence that TopDown did not threaten the ability to balance districts, describe their demographic composition accurately, or detect signals of racial polarization.

Url: https://hdsr.mitpress.mit.edu/pub/954ycugm/release/1

User Submitted?: No

Authors: Cohen, Aloni; Duchin, Moon; Matthews, JN N; Suwal, Bhushan

Periodical (Full): Harvard Data Science Review

Issue: Special Issue 2

Volume:

Pages: 1-43

Data Collections: IPUMS NHGIS

Topics: Methodology and Data Collection, Other, Population Data Science

Countries: