大数据外文翻译参考文献综述文档格式.docx

大数据外文翻译参考文献综述文档格式.docx

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大数据外文翻译参考文献综述@#@大数据外文翻译参考文献综述@#@（文档含中英文对照即英文原文和中文翻译）@#@原文：

@#@@#@@#@DataMiningandDataPublishing@#@Data miningisthe extraction ofvast interestingpatterns orknowledge from huge amount of data. The initial idea ofprivacy-preservingdataminingPPDMwastoextendtraditionaldataminingtechniquestoworkwiththedatamodifiedtomasksensitiveinformation.Thekeyissueswerehowtomodifythedataandhowtorecoverthedataminingresultfromthemodifieddata.Privacy-preservingdataminingconsiderstheproblemofrunningdataminingalgorithmsonconfidentialdatathatisnotsupposedtoberevealedeventotheparty@#@runningthealgorithm.Incontrast,privacy-preservingdatapublishing（PPDP）maynotnecessarilybetiedtoaspecificdataminingtask,andthedataminingtaskmaybeunknownatthetimeofdatapublishing.PPDPstudieshowtotransformrawdataintoaversionthatisimmunizedagainstprivacyattacksbutthatstillsupportseffectivedataminingtasks.Privacy-preservingforbothdatamining（PPDM）anddatapublishing（PPDP）hasbecomeincreasinglypopularbecauseitallowssharingofprivacysensitivedataforanalysispurposes.Onewellstudiedapproachisthek-anonymitymodel[1]whichinturnledtoothermodelssuchasconfidencebounding,l-diversity,t-closeness,（α,k）-anonymity,etc.Inparticular,allknownmechanismstrytominimizeinformationlossandsuchanattemptprovidesaloopholeforattacks.Theaimofthispaperistopresentasurveyformostofthecommonattackstechniquesforanonymization-basedPPDM&@#@PPDPandexplaintheireffectsonDataPrivacy.@#@Althoughdataminingispotentiallyuseful,manydataholdersarereluctanttoprovidetheirdatafordataminingforthefearofviolatingindividualprivacy.Inrecentyears,studyhasbeenmadetoensurethatthesensitiveinformationofindividualscannotbeidentifiedeasily.@#@AnonymityModels,k-anonymizationtechniqueshavebeenthe@#@focusofintenseresearchinthelastfewyears.Inordertoensureanonymizationofdatawhileatthesametimeminimizingtheinformation@#@lossresultingfromdatamodifications,everalextendingmodelsareproposed,whicharediscussedasfollows.@#@1.k-Anonymity@#@k-anonymityisoneofthemostclassicmodels,whichtechniquethatpreventsjoiningattacksbygeneralizingand/orsuppressingportionsofthereleasedmicrodatasothatnoindividualcanbeuniquelydistinguishedfromagroupofsizek.Inthek-anonymoustables,adatasetisk-anonymous（k≥1）ifeachrecordinthedatasetisin -distinguishablefromatleast（k.1）otherrecordswithinthesamedataset.Thelargerthevalueofk,thebettertheprivacyisprotected.k-anonymitycanensurethatindividualscannotbeuniquelyidentifiedbylinkingattacks.@#@2.ExtendingModels@#@Sincek-anonymitydoesnotprovidesufficientprotectionagainstattributedisclosure.Thenotionofl-diversityattemptstosolvethisproblem byrequiring that each equivalence class has at least lwell-representedvalueforeachsensitiveattribute.Thetechnologyofl-diversityhassomeadvantagesthank-anonymity.Becausek-anonymitydatasetpermitsstrongattacksduetolackofdiversityinthesensitiveattributes.Inthismodel,anequivalenceclassissaidtohavel-diversityifthereareatleastlwell-representedvalueforthesensitiveattribute.Becausetherearesemanticrelationshipsamongtheattributevalues,anddifferent values have very different levels of sensitivity. After@#@anonymization,inanyequivalenceclass,thefrequency（infraction）ofasensitivevalueisnomorethanα.@#@3.RelatedResearchAreas@#@Severalpollsshowthatthepublichasanin-creasedsenseofprivacyloss.Sincedataminingisoftenakeycomponentofinformationsystems,homelandsecuritysystems,andmonitoringandsurveillancesystems,itgivesawrongimpressionthatdataminingisatechniqueforprivacyintrusion.Thislackoftrusthasbecomeanobstacletothebenefitofthetechnology.Forexample,thepotentiallybeneficialdataminingre-searchproject,TerrorismInformationAwareness（TIA）,wasterminatedbytheUSCongressduetoitscontroversialproceduresofcollecting,sharing,andanalyzingthetrailsleftbyindividuals.Motivatedbytheprivacyconcernsondataminingtools,aresearchareacalledprivacy-reservingdatamining（PPDM）emergedin2000.TheinitialideaofPPDMwastoextendtraditionaldataminingtechniquestoworkwiththedatamodifiedtomasksensitiveinformation.Thekeyissueswerehowtomodifythedataandhowtorecoverthe dataminingresultfromthemodifieddata.@#@Thesolutionswereoftentightlycoupledwiththedataminingalgorithmsunderconsideration.Incontrast,privacy-preservingdatapublishing（PPDP）maynotnecessarilytietoaspecificdataminingtask,andthedataminingtaskissometimesunknownatthetim