Full Citation

Title: Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product

Citation Type: Conference Paper

Publication Year: 2020

ISBN: 9783030575205

ISSN: 16113349

DOI: 10.1007/978-3-030-57521-2_25

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Abstract: Producing accurate, usable data while protecting respondent privacy are dual mandates of the US Census Bureau. In 2019, the Census Bureau announced it would use a new disclosure avoidance technique, based on differential privacy, for the 2020 Decennial Census of Population and Housing[19]. Instead of suppressing data or swapping sensitive records, differentially private methods inject noise into counts to protect privacy. Unfortunately, noise injection may also make the data less useful and accurate. This paper describes the differentially private Disclosure Avoidance System (DAS) used to prepare the 2010 Demonstration Data Product (DDP). It describes the policy decisions that underlie the DAS and how the DAS uses those policy decisions to produce differentially private data. Finally, it discusses usability and accuracy issues in the DDP, with a focus on occupied housing unit counts. Occupied housing unit counts in the DDP differed greatly from 2010 Summary File 1 differed greatly, and the paper explains possible sources of the differences.

Url: https://doi.org/10.1007/978-3-030-57521-2_25

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Authors: Van Riper, David; Kugler, Tracy; Ruggles, Steven

Conference Name: Privacy in Statistical Databases

Publisher Location: Springer, Cham

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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