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Making Differential Privacy Work for Census Data Users

Published onNov 01, 2023
Making Differential Privacy Work for Census Data Users
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You're viewing an older Release (#2) of this Pub.

  • This Release (#2) was created on Nov 03, 2023 ()
  • The latest Release (#4) was created on Dec 07, 2023 ().

Abstract

The U.S. Census Bureau collects and publishes detailed demographic data about Americans which are heavily used by researchers and policymakers. The Bureau has recently adopted the framework of differential privacy in an effort to improve confidentiality of individual census responses. A key output of this privacy protection system is the Noisy Measurement File (NMF), which is produced by adding random noise to tabulated statistics. The NMF is critical to understanding any errors introduced in the data, and performing valid statistical inference on published census data. Unfortunately, the current release format of the NMF is difficult to access and work with. We describe the process we use to transform the NMF into a usable format, and provide recommendations to the Bureau for how to release future versions of the NMF. These changes are essential for ensuring transparency of privacy measures and reproducibility of scientific research built on census data.

Keywords: census data, differential privacy, open science



11/03/2023: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.


©2023 Cory McCartan, Tyler Simko, and Kosuke Imai. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

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