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

6Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Van Riper, D., Kugler, T., & Ruggles, S. (2020). Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12276 LNCS, pp. 353–368). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57521-2_25

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free