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Lin Mines 0052E 11842.Pdf (8.644Mb) PRIVACY AND SECURITY IN CROWDSENSING by Jian Lin c Copyright by Jian Lin, 2019 All Rights Reserved A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Doctors of Philosophy (Computer Science). Golden, Colorado Date Signed: Jian Lin Signed: Dr. Dejun Yang Advisor Thesis Advisor Golden, Colorado Date Signed: Dr. Tracy Camp Professor and Head Department of Computer Science ii ABSTRACT The rapid proliferation of sensor-embedded devices has enabled crowdsensing, a new paradigm which effectively collects sensing data from pervasive users. However, both the openness of crowdsensing systems and the richness of users’ submitted data raise significant concerns for privacy and security. In this thesis, we aim to identify and solve privacy and security issues in crowdsensing. Specifically, we consider three important parts in crowdsens- ing: task allocation, incentive mechanisms, and truth discovery. In crowdsensing systems, task allocation is used to select a proper subset of users to perform tasks. Incentive mech- anisms are used to stimulate users to participate in the system. Truth discovery is used to aggregate data. We first analyze privacy issues in task allocation and incentive mecha- nisms raised by the inference attack in which a user is able to infer other users’ sensitive information according to published information. We propose two task allocation algorithms which defend against location-inference attack. To protect users’ bid privacy from inference attack, we propose two frameworks for privacy-preserving incentive mechanisms. Then, we analyze the security issues in incentive mechanisms and truth discovery raised by the Sybil attack in which a user illegitimately pretends to be multiple identities to gain benefits. To deter users from conducting a Sybil attack, we propose Sybil-proof incentive mechanisms for both offline and online scenarios. Additionally, we propose a Sybil-resistant truth discovery framework to diminish the impact of the Sybil attack on the aggregated data. Both simula- tion and experiment results show the effectiveness of the proposed works in solving privacy and security issues in crowdsensing. iii TABLE OF CONTENTS ABSTRACT ......................................... iii LISTOFFIGURES ..................................... ix LISTOFTABLES...................................... xi ACKNOWLEDGMENTS ..................................xii CHAPTER1 INTRODUCTION ...............................1 1.1 TaskAllocationinCrowdsensing . .....1 1.2 IncentiveMechanismsinCrowdsensing . ........2 1.3 TruthDiscoveryinCrowdsensing . ......3 1.4 PrivacyIssuesinCrowdsensing . ......3 1.4.1 PrivacyIssuesinTaskAllocation . ....3 1.4.2 Privacy Issues in Incentive Mechanisms . .......4 1.5 SecurityIssuesinCrowdsensing . .......4 1.5.1 Security Issues in Incentive Mechanisms . .......5 1.5.2 SecurityIssuesinTruthDiscovery. ......5 1.6 Contribution.................................... .6 1.7 ThesisOrganization.............................. ....7 CHAPTER2 RELATEDWORK ..............................8 2.1 PrivacyandSecurityinTaskAllocation . .......8 2.2 Privacy and Security in Incentive Mechanisms . .........10 2.3 PrivacyandSecurityinTruthDiscovery . ....... 11 iv CHAPTER 3 PRESERVING LOCATION PRIVACY IN TASK ALLOCATION . 13 3.1 Background .....................................13 3.2 ModelandProblemFormulation. .... 15 3.2.1 SystemModel................................ 15 3.2.2 AdversaryModel .............................. 15 3.2.3 ProblemFormulation. 19 3.3 OurApproach.................................... 21 3.3.1 DesignofPASTA.............................. 21 3.3.2 DesignofHeuristic ............................. 23 3.4 PerformanceEvaluation . 25 3.4.1 EvaluationSetup .............................. 25 3.4.2 ImpactoftheNumberofTasks . 26 3.4.3 ImpactoftheNumberofUsers . 28 3.4.4 ImpactofthePrivacyRequirement . 30 3.4.5 Impact of the Parameter ǫ .........................31 3.5 Conclusion...................................... 32 CHAPTER 4 PRESERVING BID PRIVACY IN INCENTIVE MECHANISMS . 33 4.1 Background .....................................33 4.2 ModelsandProblemFormulation . .... 34 4.2.1 Single-bidModel .............................. 34 4.2.2 Multi-bidModel .............................. 35 4.2.3 ThreatModels ............................... 36 4.2.4 DesiredProperties ............................. 38 v 4.2.5 DesignObjective .............................. 40 4.3 OurApproach.................................... 41 4.3.1 DesignRationale .............................. 41 4.3.2 DesignofBidGuard............................. 41 4.3.3 DesignofBidGuard-M . 44 4.4 Analysis .......................................47 4.4.1 AnalysisofBidGuard............................ 47 4.4.2 AnalysisofBidGuard-M . 52 4.5 PerformanceEvaluation . 55 4.5.1 SimulationSetup .............................. 55 4.5.2 EvaluationofSocialCost. 56 4.5.3 EvaluationofTotalPayment. 59 4.5.4 EvaluationofPrivacyLeakage . 59 4.6 Conclusion...................................... 63 CHAPTER 5 DETERRING THE SYBIL ATTACK IN INCENTIVE MECHANISMS . 65 5.1 Background .....................................65 5.2 ModelandProblemFormulation. .... 65 5.2.1 OfflineScenario ............................... 66 5.2.2 OnlineScenario ............................... 68 5.2.3 ThreatModels ............................... 68 5.2.4 DesiredPropertiesandObjective . .... 74 5.3 OurApproach.................................... 75 5.3.1 DesignofSPIM-SandSPIM-M . 75 vi 5.3.2 DesignofSOSandSOM . 80 5.4 Analysis .......................................83 5.4.1 AnalysisofSPIM-SandSPIM-M . 84 5.4.2 AnalysisofSOSandSOM . 90 5.5 Performance Evaluation of SPIM-S and SPIM-M . ...... 95 5.5.1 EvaluationSetup .............................. 96 5.5.2 EvaluationofRunningTime. 96 5.5.3 EvaluationofTotalPayment. 97 5.5.4 EvaluationofPlatformUtility . 98 5.6 PerformanceEvaluationof SOSand SOM. .... 99 5.6.1 EvaluationSetup .............................. 99 5.6.2 EvaluationofTotalPayment. 99 5.6.3 EvaluationofPlatformUtility . 100 5.6.4 EvaluationofSybil-proofness . 101 5.7 Conclusion..................................... 102 CHAPTER 6 ALLEVIATING THE SYBIL ATTACK IN TRUTH DISCOVERY . 103 6.1 Background .................................... 103 6.1.1 TruthDiscovery.............................. 103 6.1.2 DeviceFingerprinting. 105 6.2 ModelandProblemFormulation. 106 6.2.1 SystemModel............................... 106 6.2.2 AdversaryModels............................. 107 6.3 OurApproach................................... 109 vii 6.3.1 DesignRationale ............................. 109 6.3.2 DesignofFramework . 109 6.3.3 DesignofAccountGroupingMethods. 111 6.4 Experiment .................................... 120 6.4.1 ExperimentalSetup............................ 121 6.4.2 EvaluationofAccountGrouping. 122 6.4.3 EvaluationofAccuracy. 124 6.5 Conclusion..................................... 127 CHAPTER7 CONCLUSION............................... 128 7.1 SummaryofResults ............................... 128 7.2 SummaryofPublications. 129 7.3 FutureResearchOpportunities. ..... 130 REFERENCESCITED .................................. 132 APPENDIX COPYRIGHTPERMISSION . 143 viii LIST OF FIGURES Figure 1.1 Three major components in crowdsensing and their privacy and securityissuesstudiedinthisthesis . ....2 Figure 3.1 Comparison between our work and existing works . ..........14 Figure3.2 Systemmodel ................................ 16 Figure 3.3 Example of location-inference attack via task overlap ........... 17 Figure 3.4 Example of location-inference attack via task sequence........... 18 Figure 3.5 Example of mix-zone-based task allocation . ...........19 Figure 3.6 GPS locations of the taxi drivers in Rome . .........25 Figure 3.7 Impact of m on PASTA, Heuristic,andPWSM . 27 Figure 3.8 Impact of n on PASTA, Heuristic,andPWSM . 29 Figure 3.9 Impact of n on HeuristicandPWSM . 30 Figure 3.10 Impact of k on PASTAand Heuristic . 31 Figure 3.11 Impact of ǫ on PASTA............................31 Figure 4.1 Impact of the number of sensing tasks on the social cost. (a) BidGuard. (b)BidGuard-M. ............................... 57 Figure 4.2 Impact of the number of users on the social cost. (a) BidGuard. (b) BidGuard-M................................... 57 Figure 4.3 Comparison of BidGuard, TRAC, DP-hSRC and OPT. (a) Impact of the number of sensing tasks. (b) Impact of the number of users. .....58 Figure 4.4 Impact of the number of sensing tasks on the total payment. (a) BidGuard.(b)BidGuard-M. 59 Figure 4.5 Impact of the number of users on the total payment. ..........60 ix Figure 4.6 Impact of the number of sensing tasks on privacy leakage.. 61 Figure 4.7 Impact of the number of users on privacy leakage. ...........61 Figure 4.8 Impact of the ǫ onprivacyleakage.. 62 Figure4.9 Socialcostv.s. privacyleakage. .........63 Figure5.1 Onlinecrowdsensingsystem . ...... 66 Figure 5.2 Example showing MMT isnotSybil-proofinSMcase . 71 Figure 5.3 Example showing MSensing isnotSybil-proofinMMcase . 72 Figure5.4 Runningtime ................................ 97 Figure5.5 Totalpayment............................... 98 Figure5.6 Platformutility ............................ .... 98 Figure5.7 Totalpayment............................... 100 Figure5.8 Platformutility ............................ 101 Figure5.9 Sybil-proofness. .... 101 Figure 6.1 MEMS-based accelerometer and gyroscope . ........ 106 Figure6.2 ExampleofAG-FP ............................. 115 Figure6.3 ExampleofAG-TS ............................
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