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Disclosure control using partially synthetic data for large-scale health surveys, with applications to CanCORS

Loong, Bronwyn; Zaslavsky, Alan; He, Yulei; Harrington, David

Description

Statistical agencies have begun to partially synthesize public-use data for major surveys to protect the confidentiality of respondents' identities and sensitive attributes by replacing high disclosure risk and sensitive variables with multiple imputation

dc.contributor.authorLoong, Bronwyn
dc.contributor.authorZaslavsky, Alan
dc.contributor.authorHe, Yulei
dc.contributor.authorHarrington, David
dc.date.accessioned2015-12-08T22:45:10Z
dc.identifier.issn0277-6715
dc.identifier.urihttp://hdl.handle.net/1885/37708
dc.description.abstractStatistical agencies have begun to partially synthesize public-use data for major surveys to protect the confidentiality of respondents' identities and sensitive attributes by replacing high disclosure risk and sensitive variables with multiple imputation
dc.publisherJohn Wiley & Sons Inc
dc.sourceStatistics in Medicine
dc.subjectKeywords: article; cancer epidemiology; cancer research; clinical research; colorectal cancer; confidentiality; data analysis; data synthesis; health survey; human; inferential statistics; information processing; intermethod comparison; interpersonal communication; Data confidentiality; Data utility; Disclosure risk; Multiple imputation; Synthetic data
dc.titleDisclosure control using partially synthetic data for large-scale health surveys, with applications to CanCORS
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume32
dc.date.issued2013
local.identifier.absfor010401 - Applied Statistics
local.identifier.ariespublicationu4602557xPUB152
local.type.statusPublished Version
local.contributor.affiliationLoong, Bronwyn, College of Business and Economics, ANU
local.contributor.affiliationZaslavsky, Alan, Harvard Medical School
local.contributor.affiliationHe, Yulei, Harvard Medical School
local.contributor.affiliationHarrington, David, Dana-Farber Cancer Institute
local.description.embargo2037-12-31
local.bibliographicCitation.issue24 (Oct 2013)
local.bibliographicCitation.startpage4139
local.bibliographicCitation.lastpage4161
local.identifier.doi10.1002/sim.5841
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
dc.date.updated2016-02-24T11:12:56Z
local.identifier.scopusID2-s2.0-84885057181
local.identifier.thomsonID000325155700001
CollectionsANU Research Publications

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