Privacy-Preserving Algorithms for Multiple Sensitive Attributes Satisfying t-Closeness-中国期刊网

摘要 Althoughk-anonymityisagoodwayofpublishingmicrodataforresearchpurposes,itcannotresistseveralcommonattacks,suchasattributedisclosureandthesimilarityattack.Toresisttheseattacks,manyrefinementsofk-anonymityhavebeenproposedwitht-closenessbeingoneofthestrictestprivacymodels.Whilemostexistingt-closenessmodelsaddressthecaseinwhichtheoriginaldatahaveonlyonesinglesensitiveattribute,datawithmultiplesensitiveattributesaremorecommoninpractice.Inthispaper,wecoverthisgapwithtwoproposedalgorithmsformultiplesensitiveattributesandmakethepublisheddatasatisfyt-closeness.Basedontheobservationthatthevaluesofthesensitiveattributesinanyequivalenceclassmustbeasspreadaspossibleovertheentiredatatomakethepublisheddatasatisfyt-closeness,bothofthealgorithmsusedifferentmethodstopartitionrecordsintogroupsintermsofsensitiveattributes.Oneusesaclusteringmethod,whiletheotherleveragestheprincipalcomponentanalysis.Then,accordingtothesimilarityofquasi-identifierattributes,recordsareselectedfromdifferentgroupstoconstructanequivalenceclass,whichwillreducethelossofinformationasmuchaspossibleduringanonymization.Ourproposedalgorithmsareevaluatedusingarealdataset.Theresultsshowthattheaveragespeedofthefirstproposedalgorithmisslowerthanthatofthesecondproposedalgorithmbuttheformercanpreservemoreoriginalinformation.Inaddition,comparedwithrelatedapproaches,bothproposedalgorithmscanachievestrongerprotectionofprivacyandreduceless.

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Privacy-Preserving Algorithms for Multiple Sensitive Attributes Satisfying t-Closeness

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Privacy-Preserving Algorithms for Multiple Sensitive Attributes Satisfying t-Closeness

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