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Protecting Data Confidentiality in Publicly Released Datasets: Approaches Based on Multiple Imputation

Publication ,  Journal Article
Reiter, JP
January 1, 2012

Statistical organizations that release data to the public typically are required to protect the confidentiality of survey respondents' identities and attribute values. Removing direct identifiers such as names and addresses generally is not sufficient to eliminate disclosure risks, so that statistical disclosure limitation strategies must be applied to the data before release. This chapter presents an overview of how multiple imputation, originally devised to handle missing data, can be adapted for disclosure limitation. It reviews the literature on inferential methods for analyzing such datasets. It concludes with discussion of implementation challenges and topics for future research. © 2012 Elsevier B.V.

Duke Scholars

DOI

ISSN

0169-7161

Publication Date

January 1, 2012

Volume

28

Start / End Page

533 / 545

Related Subject Headings

  • Statistics & Probability
 

Citation

APA
Chicago
ICMJE
MLA
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Reiter, J. P. (2012). Protecting Data Confidentiality in Publicly Released Datasets: Approaches Based on Multiple Imputation, 28, 533–545. https://doi.org/10.1016/B978-0-44-451875-0.00020-8
Reiter, J. P. “Protecting Data Confidentiality in Publicly Released Datasets: Approaches Based on Multiple Imputation” 28 (January 1, 2012): 533–45. https://doi.org/10.1016/B978-0-44-451875-0.00020-8.
Reiter, J. P. Protecting Data Confidentiality in Publicly Released Datasets: Approaches Based on Multiple Imputation. Vol. 28, Jan. 2012, pp. 533–45. Scopus, doi:10.1016/B978-0-44-451875-0.00020-8.
Journal cover image

DOI

ISSN

0169-7161

Publication Date

January 1, 2012

Volume

28

Start / End Page

533 / 545

Related Subject Headings

  • Statistics & Probability