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dc.contributor.authorDomingo-Ferrer, Josep-
dc.contributor.authorTorra, Vicenç-
dc.date.accessioned2018-02-08T12:32:04Z-
dc.date.available2018-02-08T12:32:04Z-
dc.date.issued2003-
dc.identifierdoi: 10.1023/A:1025666923033-
dc.identifierissn: 0960-3174-
dc.identifier.citationStatistics and Computing 13: 343- 354 (2003)-
dc.identifier.urihttp://hdl.handle.net/10261/160337-
dc.description.abstractThe performance of Statistical Disclosure Control (SDC) methods for microdata (also called masking methods) is measured in terms of the utility and the disclosure risk associated to the protected microdata set. Empirical disclosure risk assessment based on record linkage stands out as a realistic and practical disclosure risk assessment methodology which is applicable to every conceivable masking method. The intruder is assumed to know an external data set, whose records are to be linked to those in the protected data set; the percent of correctly linked record pairs is a measure of disclosure risk. This paper reviews conventional record linkage, which assumes shared variables between the external and the protected data sets, and then shows that record linkage - and thus disclosure - is still possible without shared variables.-
dc.description.sponsorshipThis work has been partially supported by the European Commission under project no. IST-2000-25069 “CASC”.-
dc.publisherSpringer-
dc.rightsclosedAccess-
dc.subjectRecord linkage-
dc.subjectStatistical disclosure control-
dc.subjectDisclosure risk for microdata-
dc.subjectReidentification-
dc.titleDisclosure risk assessment in statistical microdata protection via advanced record linkage-
dc.typeartículo-
dc.date.updated2018-02-08T12:32:05Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
dc.contributor.funderEuropean Commission-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
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