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dc.contributor.authorNin Guerrero, Jordi
dc.contributor.authorTorra i Reventós, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2011-07-04T08:53:24Z
dc.date.available2011-07-04T08:53:24Z
dc.date.created2009-08-07
dc.date.issued2009-08-07
dc.identifier.citationNin, J.; Torra, V. Analysis of the univariate microaggregation disclosure risk. "New generation computing", 07 Agost 2009, vol. 27, núm. 3, p. 197-214.
dc.identifier.issn0288-3635
dc.identifier.urihttp://hdl.handle.net/2117/12855
dc.description.abstractMicroaggregation is a protection method used by statistical agencies to limit the disclosure risk of confidential information. Formally, microaggregation assigns each original datum to a small cluster and then replaces the original data with the centroid of such cluster. As clusters contain at least k records, microaggregation can be considered as preserving k-anonymity. Nevertheless, this is only so when multivariate microaggregation is applied and, moreover, when all variables are microaggregated at the same time. When different variables are protected using univariate microaggregation, k-anonymity is only ensured at the variable level. Therefore, the real k-anonymity decreases for most of the records and it is then possible to cause a leakage of privacy. Due to this, the analysis of the disclosure risk is still meaningful in microaggregation. This paper proposes a new record linkage method for univariate microaggregation based on finding the optimal alignment between the original and the protected sorted variables. We show that our method, which uses a DTW distance to compute the optimal alignment, provides the intruder with enough information in many cases to to decide if the link is correct or not. Note that, standard record linkage methods never ensure the correctness of the linkage. Furthermore, we present some experiments using two well-known data sets, which show that our method has better results (larger number of correct links) than the best standard record linkage method.
dc.format.extent18 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Seguretat informàtica
dc.subject.lcshData protection
dc.subject.otherPrivacy on statistical databases
dc.subject.otherPrivacy preserving data mining
dc.subject.otherRecord linkage
dc.subject.otherMicroaggregation
dc.subject.otherDTW distance
dc.titleAnalysis of the univariate microaggregation disclosure risk
dc.typeArticle
dc.subject.lemacProtecció de dades
dc.identifier.doi10.1007/s00354-007-0061-1
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac2593579
dc.description.versionPostprint (published version)
local.citation.authorNin, J.; Torra, V.
local.citation.publicationNameNew generation computing
local.citation.volume27
local.citation.number3
local.citation.startingPage197
local.citation.endingPage214


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