简介：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.

简介：Whilecloud-basedBPM(BusinessProcessManagement)showspotentialsofinherentscalabilityandexpenditurereduction,suchissuesasuserautonomy,privacyprotectionandefficiencyhavepoppedupasmajorconcerns.Usersmayhavetheirownrudimentaryorevenfull-edgedBPMsystems,whichmaybeembodiedbylocalEAIsystems,attheirend,butstillintendtomakeuseofcloud-sideinfrastructureservicesandBPMcapabilities,whichmayappearasPaaS(Platform-as-a-Service)services,atthesametime.Awholebusinessprocessmaycontainanumberofnon-compute-intensiveactivities,forwhichcloudcomputingisover-provision.Moreover,someusersfeardataleakageandlossofprivacyiftheirsensitivedataisprocessedinthecloud.Thispaperproposesandanalyzesanovelarchitectureofcloud-basedBPM,whichsupportsuser-enddistributionofnon-compute-intensiveactivitiesandsensitivedata.Anapproachtooptimaldistributionofactivitiesanddataforsyntheticallyutilizingbothuser-endandcloud-sideresourcesisdiscussed.Experimentalresultsshowthatwiththehelpofsuitabledistributionschemes,dataprivacycanbesatisfactorilyprotected,andresourcesonbothsidescanbeutilizedatlowercost.