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dc.contributor.authorBalasch Masoliver, Jordi
dc.contributor.authorMuntés Mulero, Víctor
dc.contributor.authorNin Guerrero, Jordi
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2014-12-12T12:11:36Z
dc.date.available2014-12-12T12:11:36Z
dc.date.created2014-01-01
dc.date.issued2014-01-01
dc.identifier.citationBalasch, J.; Muntés, V.; Nin, J. Using genetic algorithms for attribute grouping in multivariate microaggregation. "Intelligent data analysis", 01 Gener 2014, vol. 18, núm. 5, p. 819-836.
dc.identifier.issn1088-467X
dc.identifier.urihttp://hdl.handle.net/2117/25011
dc.description.abstractAnonymization techniques that provide k-anonymity suffer from loss of quality when data dimensionality is high. Microaggregation techniques are not an exception. Given a set of records, attributes are grouped into non-intersecting subsets and microaggregated independently. While this improves quality by reducing the loss of information, it usually leads to the loss of the k-anonymity property, increasing entity disclosure risk. In spite of this, grouping attributes is still a common practice for data sets containing a large number of records. Depending on the attributes chosen and their correlation, the amount of information loss and disclosure risk vary. However, there have not been serious attempts to propose a way to find the best way of grouping attributes. In this paper, we present GOMM, the Genetic Optimizer for Multivariate Microaggregation which, as far as we know, represents the first proposal using evolutionary algorithms for this problem. The goal of GOMM is finding the optimal, or near-optimal, attribute grouping taking into account both information loss and disclosure risk. We propose a way to map attribute subsets into a chromosome and a set of new mutation operations for this context. Also, we provide a comprehensive analysis of the operations proposed and we show that, after using our evolutionary approach for different real data sets, we obtain better quality in the anonymized data comparing it to previously used ad-hoc attribute grouping techniques. Additionally, we provide an improved version of GOMM called D-GOMM where operations are dynamically executed during the optimization process to reduce the GOMM execution time.
dc.format.extent18 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshGenetic algorithms
dc.subject.otherGenetic clustering algorithms
dc.subject.othermultivariate microaggregation
dc.subject.otherattribute selection
dc.subject.otherK-anonymity
dc.subject.otherDisclosure control
dc.subject.otherRecord linkage
dc.subject.otherPrivacy
dc.titleUsing genetic algorithms for attribute grouping in multivariate microaggregation
dc.typeArticle
dc.subject.lemacAlgorismes genètics
dc.contributor.groupUniversitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.3233/IDA-140670
dc.relation.publisherversionhttp://iospress.metapress.com/content/yk26174n49260128/
dc.rights.accessOpen Access
local.identifier.drac12912633
dc.description.versionPostprint (author’s final draft)
local.citation.authorBalasch, J.; Muntés, V.; Nin, J.
local.citation.publicationNameIntelligent data analysis
local.citation.volume18
local.citation.number5
local.citation.startingPage819
local.citation.endingPage836


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