Privacy Overview and Data Mining CSC 301 Spring 2018 ... · CSC 301 Spring 2018 ... Internet for...

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Privacy Overview and Data Mining CSC 301 Spring 2018 Howard Rosenthal

Transcript of Privacy Overview and Data Mining CSC 301 Spring 2018 ... · CSC 301 Spring 2018 ... Internet for...

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PrivacyOverviewandDataMining

CSC301Spring2018

HowardRosenthal

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CourseNotes:

� Muchofthematerialintheslidescomesfromthebooksandtheirassociatedsupportmaterials,belowaswellasmanyofthereferencesattheclasswebsite

Baase,SaraandHenry,Timothy,AGiftofFire:Social,Legal,andEthicalIssuesforComputingTechnology(5thEdition)Pearson,March9,2017,ISBN-13:978-0134615271Quinn,Michael,EthicsfortheInformationAge(7thEdition),Pearson,Feb.21,2016,ISBN-13978-0134296548

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LessonGoals

�  Basicprinciplesinprivacy�  Definingprivacy�  Threatstoprivacy�  Impactsoftechnologyonprivacy�  Securingpersonalprivacy� Technologyexcursion–DataMining

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ThereAreManyAspectsToSecurityandPrivacy

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WhatIsPrivacy?�  IsprivacyaNaturalRight

�  Isprivacyatypeofproperty?�  Ifyouinvadeaperson’sprivacyitcanbeamajorcoerciveforce

�  Privacyusedtobefairlysimple�  Yourhomecouldnotbeinvaded,noryourpropertyseized,without

dueprocess�  Todayyourprivateinformationiseverywhere

�  Onthenet�  Onyourphone�  Onyourcomputer�  Inthecloud�  Inyouremployer’sdatabases�  Withthegovernment

�  Evenifthepeopleyougiveinformationtodonotmisusethatinformation,theinformationismoresusceptibletotheftviahackingorothermischiefthaneverbefore�  RecentlytheFederalGovernment’sOfficeofPersonalManagement

washackedanddetailedinformationoneveryonewithasecurityclearancewasstolen

�  Governmentacceptedverylittleresponsibilityforthistheft6

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ThereAreThreeKeyAspectsToPrivacy�  Freedomfromintrusion� Controloverinformationaboutoneself�  Freedomfromsurveillance(physical,electronic,etc.)

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OurPrivacyIsAlwaysBeingThreatened�  Therearemanythreatstoourprivacy

�  Intentionaluseormisuseofinformationbybusinessesorgovernment

�  Unauthorizedreleasetoinsidersbyinformationmaintainers�  Theftofinformationbycriminalsorhostilegovernments�  Inadvertentleakagethroughnegligenceorcarelessness

�  Ourownactions,suchaspostingtoomuchdataontheInternetforeitherbenign(B)ormalicious(M)purposes�  Givetoonecharityandtenotherswillcomeknocking(B)�  Listof“offcolor”moviesyoumayhavewatched(M)-usedtodiscredityou

�  Divorceproceeding(M)–sometimesusedbypoliticians�  Stealingfinancialdata(M)–usedtoopenloans,buyhomes,etc.allinyourname

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NewTechnologyCreatesManyNewOpportunitiesToInvadeOurPrivacy�  Someofthesethreatscombinebothlowtechtechniques,suchas

eavesdroppingorlookingoverashoulder,withhightechtechniques�  Governmentandprivatedatabases�  Sophisticatedtoolsforsurveillanceanddataanalysis�  Vulnerabilityofdata

�  Searchenginescollectmanyterabytesofdatadaily.�  Dataisanalyzedtotargetadvertisinganddevelopnewservices.�  Whogetstoseethisdata?Whyshouldwecare?�  Thissamedata,whenaggregated,createsadetailedbiographyofyou�  Datacollectedforonepurposewillfindotheruses�  Assumethateverythingincyberspaceisrecordedandreplicated

�  Youcreatenewpotentialsecurityleakseveryday�  Facebook�  E-mails�  Texts�  Mapinstructions�  Twitter�  IfinformationisonapublicWebsite,itisavailabletoeveryone

�  Ifyoupostpicturesofyourvacationwhileyouareonityoumaycomehometoanemptyhouse

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Re-identification

�  Re-identificationistheprocessofidentifyingindividualsusinganonymousdata.�  Re-identificationhasbecomemucheasierduetothequantityofinformationandpowerofdatasearchandanalysistools

�  Acollectionofsmallitemscanbeaggregatedtoprovideadetailedpicture

�  Yoursearchhistorycouldidentifywhoyouare.�  Workingbackwardsfromthemetadataisoftenpossiblewithenoughcomputingpoweranddata.

�  Reportersoftenuseanonymousdataastheyworktowardsidentifyingindividuals.

�  IfinformationisonapublicWebsite,itisoftenavailabletoeveryone

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PersonalSecurityandPrivacyAreOftenThreatenedByOurOwnActions

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EverythingYouAccessMayBeMonitored�  SearchEngines

�  Mayrecordallyoursearches�  IfyousearchforabookonAmazonyou’llgete-mailsaboutthatbookor

otherseveryfewdays�  Someofyoursearchesyoumaywanttokeepprivate

�  Lookingforanewjob�  Searchingforcertainspecificproducts�  Medicalsearches

�  Smartphones�  Areoftentransmittinglocationdata

�  Greatifaphoneislostorstolen�  Horribleifahousethiefgetsthedata

�  Passwordsandcodesforkeyaccountsareoftenstoredwithoutyourknowledgeandthenuploadedtothecloudwithotherdata�  Ifthecloudishackedyourinformationmaybeonthemarketwithoutyour

knowledge�  Contactlistscanbecompromised�  Photosmaybegatheredandsubjectedtovariousformsofanalysis

�  Software�  Manypiecesofsoftwarerecordalltypesofdata�  Thisdatamayultimatelybecollectedandanalyzed�  Sometimesitsimplysitsforgottenuntilsomeonedecidestoseewhat’sthere

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ManagingPersonalData–TerminologyandPrinciples(1)�  Personalinformationisanyinformationrelatingtoanindividualperson

�  Invisibleinformationgathering�  Datacollectedwithoutyourknowledge

�  Alwaysreadthefineprint�  Howoftendoyouclickagreewhendownloading?

�  Thisisanethicalissue�  Cookies

�  FilesaWebsitestoresonavisitor’scomputer�  Secondaryuse

�  Useofpersonalinformationforapurposeotherthanthepurposeforwhichitwasprovided�  Saleofconsumerinformationyoumarketersorotherbusinesses�  Useofinformationinvariousdatabasestodenysomeoneajob�  UseofvehicleregistrationsbytheIRStofindpersonswithhigh

incomes�  Useoftextmessagestoprosecuteforacrime�  Usingyourinformationinanillegalmannerafterstealingorgleaningit

fromlegitimatesources

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ManagingPersonalData–TerminologyandPrinciples(2)� Datamining

�  Searchingandanalyzingmassesofdatatofindpatternsanddevelopnewinformationorknowledge

� Computermatching�  Combiningandcomparinginformationfromdifferentdatabases(usingsocialsecuritynumber,forexample)tomatchrecords.

� Computerprofiling�  Analyzingdatatodeterminecharacteristicsofpeoplemostlikelytoengageinacertainbehavior

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InformedConsentProvidesAnEthicalFrameworkForInformationCollection�  InformedConsent

�  Youmustagreebeforeyourinformationcanbecollectedorused�  Couldbeusedtopressureyouifyouaredeniedaservicewithout

agreeingtosharethisdata�  LoJackcollectsinformationaboutyourcarlocationcontinuously–

wasthisinformedconsent�  TheAAAtriedcollectinginformationbyaskingyouifyou’dliketo

hookdatacollectorsintoyourcar–thentheyreportedthatdatatotheinsurancesideofthehouse

�  Twocommonformsforprovidinginformedconsentareoptoutandoptin:�  Inoptoutapersonmustrequest(usuallybycheckingabox)that

anorganizationnotuseinformation.�  Inoptinthecollectoroftheinformationmayuseinformationonly

ifpersonexplicitlypermitsuse(usuallybycheckingabox).�  DiscussionQuestion:

�  Haveyouseenopt-inandopt-outchoices?Where?Howweretheyworded?Wereanyofthemdeceptive?

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FairInformationPrinciples�  Abasicsetofprinciplesforbusinessestohandledatainanethicalway�  Informpeoplewhenyoucollectdata�  Collectonlythedatathatisneeded�  Makeoptinyourdefault�  Offeroptoutmethodsthatcanbeusedatanytime

�  Itishardertoensureifalldataisdeletedifyouoptinandthenoptout

�  Keepdataonlyaslongasisneed�  Maintainaccuracyofdata�  Protectthedata.Useallreasonablesecuritymethodstodoso.

�  Developpoliciesforrespondingtolawenforcementrequests� Manygovernmentorganizationsaredevelopingguidelines

�  FTCFairInformationPracticePrinciples.pdf

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DataMining

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http://www.tutorialspoint.com/data_mining/dm_quick_guide.htm

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WhatIsDataMining?

�  Dataminingisdefinedasextractinginformationfromhugesetsofdata.�  Inotherwords,wecansaythatdataminingistheprocedureof

miningknowledgefromdata.�  Dataminingcanintegratemanydifferentdatasets

�  Theinformationorknowledgeextractedfromdataminingcanbeusedforanyofthefollowingapplications�  Profiling–Thisiswhereprivacyreallygetsinvolved�  CustomerRetention�  PatternAnalysis�  MarketAnalysis�  FraudDetection�  ProductionControl�  ScienceExploration

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DataMiningTasks�  Dataminingdealswiththekindofpatternsthatcanbemined.Onthebasisofthekindofdatatobemined,therearetwocategoriesoffunctionsinvolvedinDataMining−�  TheDescriptiveFunctiondealswiththegeneralpropertiesofdata

inthedatabase.�  Class/ConceptDescription�  MiningofFrequentPatterns�  MiningofAssociations�  MiningofCorrelations�  MiningofClusters

�  ClassificationandPredictionistheprocessoffindingamodelthatdescribesthedataclassesorconcepts.Thepurposeistobeabletousethismodeltopredicttheclassofobjectswhoseclasslabelisunknown.Thisderivedmodelisbasedontheanalysisofsetsoftrainingdata.Thederivedmodelcanbepresentedinthefollowingforms−�  Classification(IF-THEN)Rules�  DecisionTrees�  MathematicalFormulae�  NeuralNetworks

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DescriptiveTasksInDataMining(1)�  TheClass/ConceptDescriptionreferstothedatatobeassociatedwiththe

classesorconcepts.Forexample,inacompany,theclassesofitemsforsalesincludecomputerandprinters,andconceptsofcustomersincludebigspendersandbudgetspenders.Suchdescriptionsofaclassoraconceptarecalledclass/conceptdescriptions.Thesedescriptionscanbederivedbythefollowingtwoways−�  DataCharacterizationreferstosummarizingdataofclassunderstudy.This

classunderstudyiscalledasTargetClass.�  DataDiscriminationreferstothemappingorclassificationofaclasswith

somepredefinedgrouporclass.�  MiningofFrequentPatternslooksatpatternsarethosepatternsthatoccur

frequentlyintransactionaldata.Thelistofkindoffrequentpatternsincludes�  TheFrequentItemSetisasetofitemsthatfrequentlyappeartogether,for

example,milkandbread.�  TheFrequentSubsequenceisasequenceofpatternsthatoccurfrequently

suchaspurchasingacameraisfollowedbymemorycard.�  TheFrequentSubStructurereferstodifferentstructuralforms,suchas

graphs,trees,orlattices,whichmaybecombinedwithitem−setsorsubsequences.

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DescriptiveTasksInDataMining(2)�  MiningofAssociation

�  Thisprocessreferstotheprocessofuncoveringtherelationshipamongdataanddeterminingassociationrules.

�  Associationsareusedinretailsalestoidentifypatternsthatarefrequentlypurchasedtogether,helpingtoidentifypotentialbuyers�  Forexample,aretailergeneratesanassociationrulethatshowsthat70%oftime

milkissoldwithbreadwhileonly30%oftimesarebiscuitssoldwithbread.�  MiningofCorrelations

�  Additionalanalysisperformedtouncoverinterestingstatisticalcorrelationsbetweenassociated-attribute−valuepairsorbetweentwoitemsetstoanalyzethatiftheyhavepositive,negativeornoeffectoneachother.

�  Wanttounderstandifthereisactualcausation�  MiningofClusters

�  Clusterreferstoagroupofsimilarkindofobjects.�  Clusteranalysisreferstoforminggroupofobjectsthatareverysimilar

toeachotherbutarehighlydifferentfromtheobjectsinotherclusters.

�  Cangroupbygender,age,homelocation,language,….

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ClassificationandPredictionFunctions

�  Classification−Itpredictstheclassofobjectswhoseclasslabelisunknown.Itsobjectiveistofindaderivedmodelthatdescribesanddistinguishesdataclassesorconcepts.TheDerivedModelisbasedontheanalysissetoftrainingdatai.e.thedataobjectwhoseclasslabeliswellknown.

�  Prediction−Itisusedtopredictmissingorunavailablenumericaldatavaluesratherthanclasslabels.RegressionAnalysisisgenerallyusedforprediction.Predictioncanalsobeusedfordistributiontrendsbasedonavailabledata.

�  OutlierAnalysis−Outliersmaybedefinedasthedataobjectsthatdonotcomplywiththegeneralbehaviorormodelofthedataavailable.

�  EvolutionAnalysis−Evolutionanalysisreferstothedescriptionandmodelregularitiesortrendsforobjectswhosebehaviorchangesovertime.

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DataWarehousing

� Datawarehousingistheprocessofconstructingandusingthedatawarehouse.Adatawarehouseisconstructedbyintegratingthedatafrommultipleheterogeneoussources.Itsupportsanalyticalreporting,structuredand/oradhocqueries,anddecisionmaking.� Datawarehousinginvolvesdatacleaning,dataintegration,anddataconsolidations.Tointegrateheterogeneousdatabases,wehavethefollowingtwoapproaches−�  QueryDrivenApproach�  UpdateDrivenApproach

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QueryDrivenApproach

�  Thisisthetraditionalapproachtointegrateheterogeneousdatabases.�  Buildswrappersandintegratorsontopofmultipleheterogeneous

databases.Theseintegratorsarealsoknownasmediators.�  TheprocessoftheQueryDrivenApproach

�  Whenaqueryisissuedtoaclientside,ametadatadictionarytranslatesthequeryintooneormorequeries,appropriatefortheindividualheterogeneoussiteinvolved.

�  Nowthesequeriesaremappedandsenttothelocalqueryprocessor.�  Theresultsfromheterogeneoussitesareintegratedintoaglobal

answerset.�  Advantages

�  Governmentdoesn’tgettokeepalargedatabaseofinformationonpermanentfile

�  Don’tneedtomaintainalargeITinfrastructure�  Disadvantages

�  TheQueryDrivenApproachneedscomplexintegrationandfilteringprocesses.�  Itisveryinefficientandveryexpensiveforfrequentqueries.�  Thisapproachisexpensiveforqueriesthatrequireaggregations(constant

regrouping)ofdata

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UpdateDrivenApproach�  Today'sdatawarehousesystemsfollowupdate-drivenapproachratherthan

thetraditionalapproachdiscussedearlier.�  Intheupdate-drivenapproach,theinformationfrommultipleheterogeneous

sourcesisintegratedinadvanceandstoredinawarehouse.�  Thisincludesdatascrubbing–theprocessofvalidatingdataforcorrectnessin

advance�  Thisinformationisavailablefordirectqueryingandanalysis.�  Advantages

�  Thisapproachprovideshighperformance.�  Thedatacanbecopied,processed,integrated,annotated,summarizedand

restructuredinthesemanticdatastoreinadvance.�  Inotherwords,westoredataintheway(s)wewanttolookatit

�  Queryprocessingdoesnotrequireaninterfacewiththeprocessingatthelocaloriginaldatasources.�  Muchlessintrusiveandresourceintensivetopullthedataonce,ratherthanwhenever

youwanttoquery�  Disadvantages

�  Mustmaintainalargeinfrastructuretoimport,storeandmaintaindata�  Privacyconcernssincethegovernmentnowhasaccesstosomuchdata

�  ThewholedebateonthePatriotActcenteredaroundwhetherornotthegovernmentcouldcontinuouslycollectandstoremetadatafromtheISPsandcell/land-linephoneproviders�  Apolitical/privacyargumentconflictedwithatechnicalargument

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DataWarehousingandDataMining�  OnlineAnalyticalMiningintegrateswithOnlineAnalyticalProcessing

todiscoverknowledgeacrossmultidimensionaldatabases.

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On-lineAnalyticalMining

�  On-lineAnalyticalMining(OLAM)hasthefollowingimportantattributes�  Highqualityofdataindatawarehouses

�  Thedataminingtoolsarerequiredtoworkonintegrated,consistent,andcleaneddatawhichareverycostlyinthepreprocessingofdata.

�  ThedatawarehousesconstructedbysuchpreprocessingarevaluablesourcesofhighqualitydataforOLAPanddataminingaswell.

�  Acomplexinformationprocessinginfrastructuresurroundseachdatawarehouses�  Informationprocessinginfrastructurereferstoaccessing,integration,

consolidation,andtransformationofmultipleheterogeneousdatabases,web-accessingandservicefacilities,reportingandOLAPanalysistools.

�  On-lineAnalyticalProcessing(OLAP)−basedexploratorydataanalysis�  Exploratorydataanalysisisrequiredforeffectivedatamining.�  OLAPprovidesfacilitiesfordataminingonvarioussubsetofdataandat

differentlevelsofabstraction.�  Onlineselectionofdataminingfunctions

�  IntegratingOLAPwithmultipledataminingfunctionsandonlineanalyticalminingprovidesuserswiththeflexibilitytoselectdesireddataminingfunctionsandswapdataminingtasksdynamically.

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StepsInDataMining�  DataCleaning

�  Thenoiseandinconsistentdataisremoved.�  DataIntegration

�  Multipledatasourcesarecombined.�  DataSelection

�  Datarelevanttotheanalysistaskareretrievedfromthedatabase.�  DataTransformation

�  Dataistransformedorconsolidatedintoformsappropriateforminingbyperformingsummaryoraggregationoperations.

�  DataMining�  Intelligentmethodsareappliedinordertoextractdatapatterns.

�  PatternEvaluation�  Datapatternsareevaluated.

�  KnowledgePresentation�  Knowledgeisrepresented,oftengraphically

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TheProcessofKnowledgeDiscovery

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Multi-DimensionalDatabases

� Multidimensionalstructuresuseavariationoftherelationalmodeltoorganizedataandexpresstherelationshipsbetweendata.� Morecomplexthanthetypicalrow/columnrelationaldatabase.Eachcellwithinamultidimensionalstructurecontainsaggregateddatarelatedtoelementsalongeachofitsdimensions

�  Timeisanadditionaldimensionusedintheanalysisofdata

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ExampleOfAMulti-DimensionalDatabaseStructure

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Incontro con le rappresentanze sindacali di CSC Italia

Incontro con le rappresentanze sindacali di CSC Italia

Spring Integration Spring Framework Meeting 2010 Cagliari

Spring Integration Spring Framework Meeting 2010 Cagliari

Chinar - Spring 2008

Chinar - Spring 2008

Fshge Csc Reso 1302714

Fshge Csc Reso 1302714

spring in Tokyo

spring in Tokyo

Oratoriamo spring

Oratoriamo spring

Ragno Spring 2011

Ragno Spring 2011

Catalogo 301 - Parker Hannifin Corporationsporlanonline.com/literature/international/italian/301.pdfCATALOGO 301 — Pagina 3 Valvole ad Espansione Termostatica n Cariche Termostatiche

Catalogo 301 - Parker Hannifin Corporationsporlanonline.com/literature/international/italian/301.pdfCATALOGO 301 — Pagina 3 Valvole ad Espansione Termostatica n Cariche Termostatiche

N 301 ottobre 1991

N 301 ottobre 1991

Linea Basic (YVL 301 – YVCL 301) - · PDF fileManual para el Instalador Manual para o Instalador Linea Basic (YVL 301 – YVCL 301) 17-09-10/24801770. LYNEABASIC Avvertenze generali

Linea Basic (YVL 301 – YVCL 301) - · PDF fileManual para el Instalador Manual para o Instalador Linea Basic (YVL 301 – YVCL 301) 17-09-10/24801770. LYNEABASIC Avvertenze generali

Catalogo Csc cornici

Catalogo Csc cornici

CSC 300 CSC 500 - Pazzaglia Srl... - info@pazzaglia.it CSC 500 Dimensioni macchina Lunghezza: 360 cm Larghezza: Allargamento idraulico da 107 cm a 157 cm Altezza max: 500 cm …

CSC 300 CSC 500 - Pazzaglia Srl... - [email protected] CSC 500 Dimensioni macchina Lunghezza: 360 cm Larghezza: Allargamento idraulico da 107 cm a 157 cm Altezza max: 500 cm …

Imago spring 015

Imago spring 015

Spring & Mule€¦ · Andrea Bozzoni, Spring&Mule Cagliari, 14 Luglio 2007 2 Contenuti 1 - Panoramica su un ESB 2 - Introduzione a Mule 3 - Integrazione tra Spring & Mule 3.1 - Spring

Spring & Mule€¦ · Andrea Bozzoni, Spring&Mule Cagliari, 14 Luglio 2007 2 Contenuti 1 - Panoramica su un ESB 2 - Introduzione a Mule 3 - Integrazione tra Spring & Mule 3.1 - Spring

CSC Magazine 2011

CSC Magazine 2011

2012 spring summer

2012 spring summer

Spring Summer 14

Spring Summer 14

Progetto Spring

Progetto Spring

PresentaziPresentazione Ragno Spring 2013.pdfone Ragno Spring 2013

PresentaziPresentazione Ragno Spring 2013.pdfone Ragno Spring 2013

Spring Magazine

Spring Magazine