大數據外文翻譯參考文獻綜述大數據外文翻譯參考文獻綜述(文檔含中英文對照即英文原文和中文翻譯)原文：DataMiningandDataPublishingDataminingistheextractionofvastinterestingpatternsorknowledgefromhugeamountofdata.Theinitialideaofprivacy-preservingdataminingPPDMwastoextendtraditionaldataminingtechniquestoworkwiththedatamodifiedtomasksensitiveinformation.Thekeyissueswerehowtomodifythedataandhowtorecoverthedataminingresultfromthemodifieddata.Privacy-preservingdataminingconsiderstheproblemofrunningdataminingalgorithmsonconfidentialdatathatisnotsupposedtoberevealedeventothepartyrunningthealgorithm.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-anonymizationtechniqueshavebeenthefocusofintenseresearchinthelastfewyears.Inordertoensureanonymizationofdatawhileatthesametimeminimizingtheinformationlossresultingfromdatamodifications,everalextendingmodelsareproposed,whicharediscussedasfollows.1.k-Anonymityk-anonymityisoneofthemostclassicmodels,whichtechniquethatpreventsjoiningattacksbygeneralizingand/orsuppressingportionsofthereleasedmicrodatasothatnoindividualcanbeuniquelydistinguishedfromagroupofsizek.Inthek-anonymoustables,adatasetisk-anonymous(k≥1)ifeachrecordinthedatasetisin-distinguishablefromatleast(k.1)otherrecordswithinthesamedataset.Thelargerthevalueofk,thebettertheprivacyisprotected.k-anonymitycanensurethatindividualscannotbeuniquelyidentifiedbylinkingattacks.2.ExtendingModelsSincek-anonymitydoesnotprovidesufficientprotectionagainstattributedisclosure.Thenotionofl-diversityattemptstosolvethisproblembyrequiringthateachequivalenceclasshasatleastlwell-representedvalueforeachsensitiveattribute.Thetechnologyofl-diversityhassomeadvantagesthank-anonymity.Becausek-anonymitydatasetpermitsstrongattacksduetolackofdiversityinthesensitiveattributes.Inthismodel,anequivalenceclassissaidtohavel-diversityifthereareatleastlwell-representedvalueforthesensitiveattribute.Becausetherearesemanticrelationshipsamongtheattributevalues,anddifferentvalueshaveverydifferentlevelsofsensitivity.Afteranonymization,inanyequivalenceclass,thefrequency(infraction)ofasensitivevalueisnomorethanα.3.RelatedResearchAreasSeveralpollsshowthatthepublichasanin-creasedsenseofprivacyloss.Sincedataminingisoftenakeycomponentofinformationsystems,homelandsecuritysystems,andmonitoringandsurveillancesystems,itgivesawrongimpressionthatdataminingisatechniqueforprivacyintrusion.Thislackoftrusthasbecomeanobstacletothebenefitofthetechnology.Forexample,thepotentiallybeneficialdataminingre-searchproject,TerrorismInformationAwareness(TIA),wasterminatedbytheUSCongressduetoitscontroversialproceduresofcollecting,sharing,andanalyzingthetrailsleftbyindividuals.Motivatedbytheprivacyconcernsondataminingtools,aresearchareacalledprivacy-reservingdatamining(PPDM)emergedin2000.TheinitialideaofPPDMwastoextendtraditionaldataminingtechniquestoworkwiththedatamodifiedtomasksensitiveinformation.Thekeyissueswerehowtomodifythedataandhowtorecoverthedataminingresultfromthemodifieddata.Thesolutionswereoftentightlycoupledwiththedataminingalgorithmsunderconsideration.Incontrast,privacy-preservingdatapublishing(PPDP)maynotnecessarilytietoaspecificdataminingtask,andthedataminingtaskissometimesunknownatthetimeofdatapublishing.Furthermore,somePPDPsolutionsemphasizepreservingthedatatruthfulnessattherecordlevel,butPPDMsolutionsoftendonotpreservesuchproperty.PPDPDiffersfromPPDMinSeveralMajorWaysasFollows：1)PPDPfocusesontechniquesforpublishingdata,nottechniquesfordatamining.Infact,itisexpectedthatstandarddataminingtechniquesareappliedonthepublisheddata.Incontrast,thedataholderinPPDMneedstorandomizethedatainsuchawaythatdataminingresultscanberecoveredfromtherandomizeddata.Todoso,thedataholdermustunderstandthedataminingtasksandalgorithmsinvolved.ThislevelofinvolvementisnotexpectedofthedataholderinPPDPwhousuallyisnotanexpertindatamining.2)Bothrandomizationandencryptiondonotpreservethetruthfulnessofvaluesattherecordlevel;therefore,thereleaseddataarebasicallymeaninglesstotherecipients.Insuchacase,thedataholderinPPDMmayconsiderreleasingthedataminingresultsratherthanthescrambleddata.3)PPDPprimarily“anonymizes”thedatabyhidingtheidentityofrecordowners,whereasPPDMseekstodirectlyhidethesensitivedata.ExcellentsurveysandbooksinrandomizationandcryptographictechniquesforPPDMcanbefoundintheexistingliterature.Afamilyofresearchworkcalledprivacy-preservingdistributeddatamining(PPDDM)aimsatperformingsomedataminingtaskonasetofprivatedatabasesownedbydifferentparties.ItfollowstheprincipleofSecureMultipartyComputation(SMC),andprohibitsanydatasharingotherthanthefinaldataminingresult.Cliftonetal.presentasuiteofSMCoperations,likesecuresum,securesetunion,securesizeofsetintersection,andscalarproduct,thatareusefulformanydataminingtasks.Incontrast,PPDPdoesnotperformtheactualdataminingtask,butconcernswithhowtopublishthedatasothattheanonymousdataareusefulfordatamining.WecansaythatPPDPprotectsprivacyatthedatalevelwhilePPDDMprotectsprivacyattheprocesslevel.Theyaddressdifferentprivacymodelsanddataminingscenarios.Inthefieldofstatisticaldisclosurecontrol(SDC),theresearchworksfocusonprivacy-preservingpublishingmethodsforstatisticaltables.SDCfocusesonthreetypesofdisclosures,namelyidentitydisclosure,attributedisclosure,andinferentialdisclosure.Identitydisclosureoccursifanadversarycanidentifyarespondentfromthepublisheddata.Revealingthatanindividualisarespondentofadatacollectionmayormaynotviolateconfidentialityrequirements.Attributedisclosureoccurswhenconfidentialinformationaboutarespondentisrevealedandcanbeattributedtotherespondent.Attributedisclosureistheprimaryconcernofmoststatisticalagenciesindecidingwhethertopublishtabulardata.Inferentialdisclosureoccurswhenindividualinformationcanbeinferredwithhighconfidencefromstatisticalinformationofthepublisheddata.SomeotherworksofSDCfocusonthestudyofthenon-interactivequerymodel,inwhichthedatarecipientscansubmitonequerytothesystem.Thistypeofnon-interactivequerymodelmaynotfullyaddresstheinformationneedsofdatarecipientsbecause,insomecases,itisverydifficultforadatarecipienttoaccuratelyconstructaqueryforadataminingtaskinoneshot.Consequently,thereareaseriesofstudiesontheinteractivequerymodel,inwhichthedatarecipients,includingadversaries,cansubmitasequenceofqueriesbasedonpreviouslyreceivedqueryresults.Thedatabaseserverisresponsibletokeeptrackofallqueriesofeachuseranddeterminewhetherornotthecurrentlyreceivedqueryhasviolatedtheprivacyrequirementwithrespecttoallpreviousqueries.Onelimitationofanyinteractiveprivacy-preservingquerysystemisthatitcanonlyanswerasublinearnumberofqueriesintotal;otherwise,anadversary(oragroupofcorrupteddatarecipients)willbeabletoreconstructallbut1.o(1)fractionoftheoriginaldata,whichisaverystrongviolationofprivacy.Whenthemaximumnumberofqueriesisreached,thequeryservicemustbeclosedtoavoidprivacyleak.Inthecaseofthenon-interactivequerymodel,theadversarycanissueonlyonequeryand,therefore,thenon-interactivequerymodelcannotachievethesamedegreeofprivacydefinedbyIntroductiontheinteractivemodel.Onemayconsiderthatprivacy-reservingdatapublishingisaspecialcaseofthenon-interactivequerymodel.Thispaperpresentsasurveyformostofthecommonattackstechniquesforanonymization-basedPPDM&PPDPandexplainstheireffectsonDataPrivacy.k-anonymityisusedforsecurityofrespondentsidentityanddecreaseslinkingattackinthecaseofhomogeneityattackasimplek-anonymitymodelfailsandweneedaconceptwhichpreventfromthisattacksolutionisl-diversity.Alltuplesarearrangedinwellrepresentedformandadversarywilldiverttolplacesoronlsensitiveattributes.l-diversitylimitsincaseofbackgroundknowledgeattackbecausenoonepredictsknowledgelevelofanadversary.Itisobservethatusinggeneralizationandsuppressionwealsoapplythesetechniquesonthoseattributeswhichdoesn’tneedthisextentofprivacyandthisleadstoreducetheprecisionofpublishingtable.e-NSTAM(extendedSensitiveTuplesAnonymityMethod)isappliedonsensitivetuplesonlyandreducesinformationloss,thismethodalsofailsinthecaseofmultiplesensitivetuples.Generalizationwithsuppressionisalsothecausesofdatalosebecausesuppressionemphasizeonnotreleasingvalueswhicharenotsuitedforkfactor.Futureworksinthisfrontcanincludedefininganewprivacymeasurealongwithl-diversityformultiplesensitiveattributeandwewillfocustogeneralizeattributeswithoutsuppressionusingothertechniqueswhichareusedtoachievek-anonymitybecausesuppressionleadstoreducetheprecisionofpublishingtable.

譯文：數據挖掘和數據發布數據挖掘中提取出大量有趣的模式從大量的數據或知識。數據挖掘隱私保護PPDM的最初的想法是將傳統的數據挖掘技術擴展到處理數據修改為屏蔽敏感信息。關鍵問題是如何修改數據以及如何從修改后的數據恢復數據挖掘的結果。隱私保護數據挖掘認為機密數據上運行數據挖掘算法的問題不應該透露方運行算法。相比之下,隱私保護數據發布(PPDP)不一定是綁定到一個特定的數據挖掘任務,和數據挖掘任務時可能是未知的數據發布。PPDP研究如何將原始數據轉換成一個版本接種隱私攻擊,但仍然支持有效的數據挖掘任務。隱私保護數據挖掘(PPDM)和數據發布(PPDP)已成為越來越受歡迎,因為它允許共享隱私的敏感數據進行分析的目的。深入研究方法之一是k-anonymity匿名模型進而導致信心邊界等模型,l-diversity,t-closeness,(α,k)-anonymity,等。特別是,所有已知的機制,盡量減少信息損失,試圖提供一個漏洞攻擊。本文的目的是提出一項調查最常見的攻擊技術即PPDM&PPDP和解釋它們對數據隱私的影響。盡管數據挖掘可能是有用的,很多數據持有者不愿提供他們的數據對數據挖掘的恐懼侵犯個人隱私。近年來,研究了以確保個人敏感信息不能輕易識別。匿名模型（k-匿名）技術一直是研究的焦點,在過去的幾年里。為了確保匿名數據的同時盡量減少所造成的信息損失數據的修改,提出了幾個擴展模型,討論如下。1.k-匿名模型k-anonymity最經典模型之一,加入的攻擊技術,防止泛化和/或抑制微數據發布的一部分,這樣任何個人可以獨特區別一群大小k。k-anonymous表,一個數據集是k-anonymous(k≥1)如果每個記錄的數據集——至少(k區分開來)其他相同的數據集內的記錄。k值越大,更好的隱私保護。英蒂k-anonymity可以確保——viduals不能唯一標識鏈接攻擊。2.擴展模型因為k-anonymity不提供足夠的保護屬性披露。l-diversity的概念試圖解決這個問題,要求每個等價類至少l上流每個敏感屬性值。比k-anonymityl-diversity技術有一定的優勢。因為k-anonymity數據集允許強大的攻擊由于缺乏多樣性的敏感屬性。在這個模型中,一個等價類據說l-diversity如果至少有l上流的敏感屬性的值。因為有語義屬性值之間的關系,以及不同價值觀有不同水平的敏感性。anonymization之后,在任何等價類,一個敏感的頻率(分數)值不超過α。3.相關研究領域一些民意調查顯示,公眾有——有折痕的隱私的失落感。由于數據挖掘通常是信息系統的一個關鍵組成部分,國土安全系統,以及監測和監測系統,它給了一個錯誤的印象,荷蘭國際集團數據隱私入侵的技術。這種缺乏信任已經成為障礙的技術中獲益。例如,潛在的有益的數據挖掘,搜索項目,恐怖主義信息意識(TIA),是由美國國會終止由于其爭議的程序收集、分享和分析個人留下的痕跡。出于隱私問題的數據挖掘工具,一個叫隱私保護的數據挖掘研究領域(PPDM)出現在2000年。PPDM的最初的想法是將傳統的數據挖掘技術擴展到處理數據修改為屏蔽敏感信息。關鍵問題是如何修改數據以及如何從修改后的數據恢復數據挖掘的結果。這些解決方案通常與數據挖掘算法在考慮緊密耦合。相比之下,隱私保護數據發布(PPDP)不一定綁到一個特定的數據挖掘任務,和數據挖掘任務有時是未知的數據發布的時候。此外,一些PPDP解決方案強調保存數據記錄級別的真實性,但是PPDM解決方案通常不保留這樣的財產。PPDP有別于PPDM在幾個主要方面如下:1)PPDP關注技術發布數據,數據挖掘技術。事實上,它預計,標準的數據挖掘技術應用于分析數據。相反,數據持有人在PPDM需要隨機數據的方式,數據挖掘結果可以從隨機數據中恢復過來。為此,持有人必須了解數據挖掘任務的數據和算法。這種級別的預計數據持有人參與PPDP通常不是一個數據挖掘專家。2)隨機化和加密不保存記錄的真實值水平;因此,公布的數據基本上是毫無意義的決策。在這種情況下,數據持有人PPDM