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

David Van Riper; Tracy Kugler; Steven Ruggles

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

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

David Van Riper, Tracy Kugler, Steven Ruggles

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

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.

Original language	English (US)
Title of host publication	Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings
Editors	Josep Domingo-Ferrer, Krishnamurty Muralidhar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	353-368
Number of pages	16
ISBN (Print)	9783030575205
DOIs	https://doi.org/10.1007/978-3-030-57521-2_25
State	Published - 2020
Event	International Conference on Privacy in Statistical Databases, PSD 2020 - Tarragona, Spain Duration: Sep 23 2020 → Sep 25 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12276 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Privacy in Statistical Databases, PSD 2020
Country/Territory	Spain
City	Tarragona
Period	9/23/20 → 9/25/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

2020 US Decennial Census
Accuracy
Differential privacy

Publisher link

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

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Van Riper, D., Kugler, T., & Ruggles, S. (2020). Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product. In J. Domingo-Ferrer, & K. Muralidhar (Eds.), Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings (pp. 353-368). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12276 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57521-2_25

Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product. / Van Riper, David ; Kugler, Tracy ; Ruggles, Steven.
Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. ed. / Josep Domingo-Ferrer; Krishnamurty Muralidhar. Springer Science and Business Media Deutschland GmbH, 2020. p. 353-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12276 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Van Riper, D , Kugler, T & Ruggles, S 2020, Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product. in J Domingo-Ferrer & K Muralidhar (eds), Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12276 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 353-368, International Conference on Privacy in Statistical Databases, PSD 2020, Tarragona, Spain, 9/23/20. https://doi.org/10.1007/978-3-030-57521-2_25

Van Riper D , Kugler T , Ruggles S. Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product. In Domingo-Ferrer J, Muralidhar K, editors, Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 353-368. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-57521-2_25

Van Riper, David ; Kugler, Tracy ; Ruggles, Steven. / Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product. Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. editor / Josep Domingo-Ferrer ; Krishnamurty Muralidhar. Springer Science and Business Media Deutschland GmbH, 2020. pp. 353-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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