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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 languageEnglish (US)
Title of host publicationPrivacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings
EditorsJosep Domingo-Ferrer, Krishnamurty Muralidhar
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030575205
StatePublished - 2020
EventInternational Conference on Privacy in Statistical Databases, PSD 2020 - Tarragona, Spain
Duration: Sep 23 2020Sep 25 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12276 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Privacy in Statistical Databases, PSD 2020

Bibliographical note

Funding Information:
Supported by the Minnesota Population Center (R24 HD041023), funded through grants from the Eunice Kennedy Shriver National Institute for Child Health and Human Development.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Copyright 2020 Elsevier B.V., All rights reserved.


  • 2020 US Decennial Census
  • Accuracy
  • Differential privacy


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