privacy-preserving data mining: models and - Charu Aggarwal

12 Pages·2014·0.05 MB·English

PRIVACY-PRESERVING DATA MINING:
MODELS AND ALGORITHMS

PRIVACY-PRESERVING DATA MINING:
MODELS AND ALGORITHMS
Editedby
CHARUC.AGGARWAL
IBMT.J.WatsonResearchCenter,Hawthorne,NY10532
PHILIPS.YU
UniversityofIllinoisatChicago,Chicago,IL60607
KluwerAcademicPublishers
Boston/Dordrecht/London
Contents
ListofFigures xv
ListofTables xx
Preface xxi
1
AnIntroductiontoPrivacy-PreservingDataMining 1
CharuC.Aggarwal,PhilipS.Yu
1. Introduction 1
2. Privacy-PreservingDataMiningAlgorithms 3
3. ConclusionsandSummary 7
References 8
2
AGeneralSurveyofPrivacy-PreservingDataMiningModelsandAlgorithms 11
CharuC.Aggarwal,PhilipS.Yu
1. Introduction 11
2. TheRandomizationMethod 13
2.1 PrivacyQuantification 15
2.2 AdversarialAttacksonRandomization 18
2.3 RandomizationMethodsforDataStreams 18
2.4 MultiplicativePerturbations 19
2.5 DataSwapping 19
3. GroupBasedAnonymization 20
3.1 Thek-AnonymityFramework 20
3.2 PersonalizedPrivacy-Preservation 24
3.3 UtilityBasedPrivacyPreservation 24
3.4 SequentialReleases 25
3.5 Thel-diversityMethod 26
3.6 Thet-closenessModel 27
3.7 ModelsforText,BinaryandStringData 27
4. DistributedPrivacy-PreservingDataMining 28
4.1 Distributed Algorithms over Horizontally Partitioned Data
Sets 30
4.2 DistributedAlgorithmsoverVerticallyPartitionedData 31
4.3 DistributedAlgorithmsfork-Anonymity 31
5. Privacy-PreservationofApplicationResults 32
5.1 AssociationRuleHiding 33
5.2 DowngradingClassifierEffectiveness 34
vi PRIVACY-PRESERVINGDATAMINING:MODELSANDALGORITHMS
5.3 QueryAuditingandInferenceControl 34
6. LimitationsofPrivacy: TheCurseofDimensionality 37
7. ApplicationsofPrivacy-PreservingDataMining 38
7.1 MedicalDatabases: TheScrubandDataflySystems 38
7.2 BioterrorismApplications 40
7.3 HomelandSecurityApplications 40
7.4 GenomicPrivacy 42
8. Summary 43
References 43
3
ASurveyofInferenceControlMethodsforPrivacy-PreservingDataMining 53
JosepDomingo-Ferrer
1. Aclassificationofmicrodataprotectionmethods 55
2. Perturbativemaskingmethods 58
2.1 Additivenoise 58
2.2 Microaggregation 59
2.3 Dataswappingandrankswapping 61
2.4 Rounding 62
2.5 Resampling 62
2.6 PRAM 62
2.7 MASSC 63
3. Non-perturbativemaskingmethods 63
3.1 Sampling 64
3.2 Globalrecoding 64
3.3 Topandbottomcoding 65
3.4 Localsuppression 65
4. Syntheticmicrodatageneration 65
4.1 Syntheticdatabymultipleimputation 65
4.2 Syntheticdatabybootstrap 66
4.3 SyntheticdatabyLatinHypercubeSampling 66
4.4 PartiallysyntheticdatabyCholeskydecomposition 67
4.5 Otherpartiallysyntheticandhybridmicrodataapproaches 67
4.6 Prosandconsofsyntheticmicrodata 68
5. Tradingoffinformationlossanddisclosurerisk 69
5.1 Scoreconstruction 69
5.2 R-Umaps 71
5.3 k-anonymity 71
6. Conclusionsandresearchdirections 72
References 73
4
MeasuresofAnonymity 81
SureshVenkatasubramanian
1. Introduction 81
1.1 Whatisprivacy? 81
1.2 DataAnonymizationMethods 83
1.3 AClassificationOfMethods 84
2. StatisticalMeasuresofAnonymity 85
Contents vii
2.1 QueryRestriction 85
2.2 AnonymityviaVariance 85
2.3 AnonymityviaMultiplicity 86
3. ProbabilisticMeasuresofAnonymity 86
3.1 MeasuresBasedonRandomPerturbation 87
3.2 MeasuresBasedonGeneralization 90
3.3 UtilityvsPrivacy 93
4. ComputationalMeasuresOfAnonymity 94
4.1 AnonymityviaIsolation 96
5. ConclusionsAndNewDirections 97
5.1 NewDirections 98
References 98
5
k-AnonymousDataMining: ASurvey 103
V.Ciriani,S.DeCapitanidiVimercati,S.Foresti,andP.Samarati
1. Introduction 103
2. k-Anonymity 105
3. AlgorithmsforEnforcingk-Anonymity 108
4. k-AnonymityThreatsfromDataMining 115
4.1 AssociationRules 116
4.2 ClassificationMining 116
5. k-AnonymityinDataMining 118
6. Anonymize-and-Mine 121
7. Mine-and-Anonymize 124
7.1 Enforcingk-AnonymityonAssociationRules 124
7.2 Enforcingk-AnonymityonDecisionTrees 127
8. Conclusions 130
Acknowledgments 131
References 131
6
ASurveyofRandomizationMethodsforPrivacy-PreservingDataMining 135
CharuC.Aggarwal,PhilipS.Yu
1. Introduction 135
2. ReconstructionMethodsforRandomization 137
2.1 TheBayesReconstructionMethod 137
2.2 TheEMReconstructionMethod 139
2.3 UtilityandOptimalityofRandomizationModels 141
3. ApplicationsofRandomization 142
3.1 Privacy-PreservingClassificationwithRandomization 142
3.2 Privacy-PreservingOLAP 143
3.3 CollaborativeFiltering 143
4. ThePrivacy-InformationLossTradeoff 144
5. VulnerabilitiesoftheRandomizationMethod 147
6. RandomizationofTimeSeriesDataStreams 149
7. MultiplicativeNoiseforRandomization 150
7.1 VulnerabilitiesofMultiplicativeRandomization 151
viii PRIVACY-PRESERVINGDATAMINING:MODELSANDALGORITHMS
7.2 SketchBasedRandomization 151
8. ConclusionsandSummary 152
References 152
7
ASurveyofMultiplicative 155
Perturbationfor
Privacy-PreservingDataMining
KekeChenandLingLiu
1. Introduction 156
1.1 DataPrivacyvs. DataUtility 157
1.2 Outline 158
2. DefinitionofMultiplicativePerturbation 159
2.1 Notations 159
2.2 RotationPerturbation 159
2.3 ProjectionPerturbation 160
2.4 Sketch-basedApproach 162
2.5 GeometricPerturbation 162
3. TransformationInvariantDataMiningModels 163
3.1 DefinitionofTransformationInvariantModels 164
3.2 Transformation-InvariantClassificationModels 164
3.3 Transformation-InvariantClusteringModels 165
4. PrivacyEvaluationforMultiplicativePerturbation 166
4.1 AConceptualMultidimensionalPrivacyEvaluationModel 166
4.2 VarianceofDifferenceasColumnPrivacyMetric 167
4.3 IncorporatingAttackEvaluation 168
4.4 OtherMetrics 169
5. AttackResilientMultiplicativePerturbations 169
5.1 NaiveEstimationtoRotationPerturbation 169
5.2 ICA-BasedAttacks 171
5.3 Distance-InferenceAttacks 172
5.4 AttackswithMorePriorKnowledge 174
5.5 FindingAttack-ResilientPerturbations 175
6. Conclusion 175
References 176
8
ASurveyofQuantificationofPrivacyPreservingDataMiningAlgorithms 181
ElisaBertinoandDanLinandWeiJiang
1. MetricsforQuantifyingPrivacyLevel 184
1.1 DataPrivacy 184
1.2 ResultPrivacy 189
2. MetricsforQuantifyingHidingFailure 190
3. MetricsforQuantifyingDataQuality 191
3.1 QualityoftheDataResultingfromthePPDMProcess 191
3.2 QualityoftheDataMiningResults 196
4. ComplexityMetrics 198
5. HowtoSelectaProperMetric 199
6. ConclusionandResearchDirections 200
References 200
Contents ix
9
ASurveyofUtility-based 205
Privacy-PreservingData
TransformationMethods
MingHuaandJianPei
1. Introduction 206
1.1 WhatisUtility-basedPrivacyPreservation? 207
2. TypesofUtility-basedPrivacyPreservationMethods 208
2.1 PrivacyModels 208
2.2 UtilityMeasures 210
2.3 SummaryoftheUtility-BasedPrivacyPreservingMethods 212
3. Utility-BasedAnonymizationUsingLocalRecoding 212
3.1 GlobalRecodingandLocalRecoding 213
3.2 UtilityMeasure 214
3.3 AnonymizationMethods 215
3.4 SummaryandDiscussion 217
4. TheUtility-basedPrivacyPreservingMethodsinClassificationProb-
lems 217
4.1 TheTop-DownSpecializationMethod 218
4.2 TheProgressiveDisclosureAlgorithm 222
4.3 SummaryandDiscussion 226
5. AnonymizedMarginal: InjectingUtilityintoAnonymizedDataSets 226
5.1 AnonymizedMarginal 227
5.2 UtilityMeasure 228
5.3 InjectingUtilityUsingAnonymizedMarginals 229
5.4 SummaryandDiscussion 231
6. Summary 232
References 232
10
MiningAssociationRulesunderPrivacyConstraints 237
JayantR.Haritsa
1. ProblemFramework 238
2. EvolutionoftheLiterature 244
3. TheFRAPPFramework 249
4. SampleResults 257
5. ClosingRemarks 261
References 261
11
ASurveyofAssociationRuleHidingMethodsforPrivacy 265
VassiliosS.VerykiosandArisGkoulalas-Divanis
1. Introduction 265
2. TerminologyandPreliminaries 267
3. TaxonomyofAssociationRuleHidingAlgorithms 268
4. ClassesofAssociationRuleAlgorithms 269
4.1 HeuristicApproaches 270
4.2 Border-basedApproaches 275
4.3 ExactApproaches 276
x PRIVACY-PRESERVINGDATAMINING:MODELSANDALGORITHMS
5. OtherHidingApproaches 277
6. MetricsandPerformanceAnalysis 279
7. DiscussionandFutureTrends 282
8. Conclusions 283
References 284
12
ASurveyofStatistical 289
ApproachestoPreserving
ConfidentialityofContingency
TableEntries
StephenE.FienbergandAleksandraB.Slavkovic
1. Introduction 289
2. TheStatisticalApproachPrivacyProtection 290
3. DataminingAlgorithms,AssociationRules,andDisclosureLimita-
tion 292
4. Estimation and Disclosure Limitation for Multi-way Contingency
Tables 293
5. TwoIllustrativeExamples 299
5.1 Example1: DatafromaRandomizedClinicalTrial 299
5.2 Example 2: Data from the 1993 U.S. Current Population
Survey 303
6. Conclusions 306
References 307
13
ASurveyof 313
Privacy-PreservingMethods
AcrossHorizontallyPartitioned
Data
MuratKantarcioglu
1. Introduction 313
2. BasicCryptographicTechniquesforPrivacy-PreservingDistributed
DataMining 315
3. CommonSecureSub-protocolsUsedinPrivacy-PreservingDistributed
DataMining 318
4. Privacy-preservingDistributed Data Mining on HorizontallyParti-
tionedData 323
5. ComparisontoVerticallyPartitionedDataModel 326
6. ExtensiontoMaliciousParties 327
7. LimitationsoftheCryptographicTechniquesUsedinPrivacy-Preserving
DistributedDataMining 329
8. PrivacyIssuesRelatedtoDataMiningResults 330
9. Conclusion 332
References 332
14
ASurveyof 337
Privacy-PreservingMethods
acrossVerticallyPartitioned
Data
Contents xi
JaideepVaidya
1. Classification 339
1.1 Na¨ıveBayesClassification 342
1.2 BayesianNetworkStructureLearning 343
1.3 DecisionTreeClassification 344
2. Clustering 346
3. AssociationRuleMining 347
4. Outlierdetection 349
4.1 Algorithm 351
4.2 SecurityAnalysis 352
4.3 ComputationandCommunicationAnalysis 354
5. ChallengesandResearchDirections 355
References 356
15
ASurveyofAttackTechniquesonPrivacy-PreservingDataPerturbation 361
Methods
KunLiu,ChrisGiannella,andHillolKargupta
1. Introduction 362
2. DefinitionsandNotation 362
3. AttackingAdditiveDataPerturbation 363
3.1 Eigen-AnalysisandPCAPreliminaries 364
3.2 SpectralFiltering 365
3.3 SVDFiltering 366
3.4 PCAFiltering 367
3.5 MAPEstimationAttack 368
3.6 DistributionAnalysisAttack 369
3.7 Summary 370
4. AttackingMatrixMultiplicativeDataPerturbation 371
4.1 KnownI/OAttacks 372
4.2 KnownSampleAttack 375
4.3 OtherAttacksBasedonICA 376
4.4 Summary 377
5. Attackingk-Anonymization 378
6. Conclusion 379
Acknowledgments 379
References 379
16
PrivateDataAnalysisvia 385
OutputPerturbation
KobbiNissim
1. TheAbstractModel–StatisticalDatabases,Queries,andSanitizers 387
2. Privacy 390
2.1 InterpretingthePrivacyDefinition 392
3. TheBasicTechnique: CalibratingNoisetoSensitivity 396
3.1 Applications: FunctionswithLowGlobalSensitivity 398
4. ConstructingSanitizersforComplexFunctionalities 402
4.1 k-MeansClustering 403