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dc.contributor.authorDomingo-Ferrer, Josep-
dc.contributor.authorTorra, Vicenç-
dc.date.accessioned2018-02-21T15:32:51Z-
dc.date.available2018-02-21T15:32:51Z-
dc.date.issued2003-
dc.identifierdoi: 10.1016/S0020-0255(03)00064-1-
dc.identifierissn: 0020-0255-
dc.identifier.citationInformation Sciences 151: 153- 170 (2003)-
dc.identifier.urihttp://hdl.handle.net/10261/161147-
dc.description.abstractStatistical disclosure control (SDC) and artificial intelligence (AI) use similar tools for different purposes. This work describes the common elements of both areas to increase their synergy. SDC is a discipline that seeks to modify statistical data so that they can be published (typically by National Statistical Offices) without giving away the identity of any individual behind the data. When dealing with individual data (microdata in SDC jargon), both SDC procedures and AI knowledge integration procedures use similar principles for different purposes (masking data vs. improving its quality). Similarities can also be found for methods evaluating re-identification risk in SDC and data mining tools for making data consistent. This paper explores those methodological connections with the aim of stimulating interaction between both fields. In particular, data mining turns out to be a common interest of both fields. © 2003 Elsevier Science Inc. All rights reserved.-
dc.description.sponsorshipThis work is partially supported by the European Commission through project “CASC” (IST-2000-25069) and by the Spanish MCyT and the FEDER fund through project “STREAMOBILE” (TIC2001-0633-C03-01/02). Comments by the reviewers are gratefully acknowledged.-
dc.publisherElsevier-
dc.rightsclosedAccess-
dc.subjectStatistical disclosure control-
dc.subjectRe-identification procedures-
dc.subjectOfficial statistics-
dc.subjectData cleaning-
dc.subjectSynthesis of information-
dc.subjectData mining-
dc.subjectArtificial intelligence-
dc.titleOn the connections between statistical disclosure control for microdata and some artificial intelligence tools-
dc.typeartículo-
dc.identifier.doi10.1016/S0020-0255(03)00064-1-
dc.date.updated2018-02-21T15:32:51Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
dc.contributor.funderMinisterio de Ciencia y Tecnología (España)-
dc.contributor.funderEuropean Commission-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100006280es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.type.coarhttp://purl.org/coar/resource_type/c_6501es_ES
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.openairetypeartículo-
item.grantfulltextnone-
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