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dc.contributor.authorPont Tuset, Jordi
dc.contributor.authorNin Guerrero, Jordi
dc.contributor.authorMedrano Gracia, Pau
dc.contributor.authorLarriba Pey, Josep
dc.contributor.authorMuntés Mulero, Víctor
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2011-01-17T10:06:51Z
dc.date.available2011-01-17T10:06:51Z
dc.date.created2008
dc.date.issued2008
dc.identifier.citationPont, J. [et al.]. Improving microaggregation for complex record anonymization. A: International Conference on Modeling Decisions for Artificial Intelligence. "The 5th International Conference on Modeling Decisions for Artificial Intelligence". Sabadell: Springer Verlag, 2008, p. 215-226.
dc.identifier.urihttp://hdl.handle.net/2117/11054
dc.description.abstractMicroaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the cluster. Usually, when records are complex, i.e., the number of attributes of the data set is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. In this way, the information loss when collapsing several values to the centroid of their group is reduced, at the cost of losing the k-anonymity property when at least two attributes of different blocks are known by the intruder. In this work, we present a new microaggregation method called One dimension microaggregation (Mic1D − κ). This method gathers all the values of the data set into a single sorted vector, independently of the attribute they belong to. Then, it microaggregates all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.
dc.format.extent12 p.
dc.language.isoeng
dc.publisherSpringer Verlag
dc.subjectÀrees temàtiques de la UPC::Informàtica::Seguretat informàtica
dc.subject.lcshData protection -- Mathematical models
dc.subject.lcshMicroaggregation
dc.subject.lcshk-anonymity
dc.subject.lcshPrivacy in statistical databases.
dc.titleImproving microaggregation for complex record anonymization
dc.typeConference report
dc.subject.lemacProtecció de dades -- Mètodes estadístics
dc.contributor.groupUniversitat Politècnica de Catalunya. DMAG - Grup d'Aplicacions Multimèdia Distribuïdes
dc.contributor.groupUniversitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.identifier.doi10.1007/978-3-540-88269-5_20
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.springerlink.com/content/2452417023322q28/
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac4498611
dc.description.versionPostprint (published version)
local.citation.authorPont, J.; Nin, J.; Medrano, P.; Larriba, J.; Muntés, V.
local.citation.contributorInternational Conference on Modeling Decisions for Artificial Intelligence
local.citation.pubplaceSabadell
local.citation.publicationNameThe 5th International Conference on Modeling Decisions for Artificial Intelligence
local.citation.startingPage215
local.citation.endingPage226


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