Census TopDown: The Impacts of Differential Privacy on Redistricting

TL;DR

This paper examines how the new differential privacy algorithm 'TopDown' affects redistricting, showing it maintains key data utility for districting and voting rights enforcement despite data noise.

Contribution

It provides an analysis of TopDown's impact on redistricting applications, offering tools and demonstrations for practitioners concerned about privacy-induced data distortions.

Findings

TopDown preserves population balance in districts.

It allows detection of racial polarization signals.

The privacy algorithm does not significantly hinder voting rights enforcement.

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.