Zhirun Zheng: Perturbation Based Privacy in Crowdsensing, Gebunden

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Chapter 1.- 1.1 An Overview of Crowdsensing.- 1.1.1 Evolutionary Path of Crowdsensing.- 1.1.2 Architecture and Characteristics of Crowdsensing.- 1.1.3 Applications of Crowdsensing.- 1.2 Privacy Challenges in Crowdsensing.- 1.2.1 Privacy Leakage.- 1.2.2 Data Privacy vs. Data Utility.- 1.2.3 Data Privacy vs. Data Poisoning.- 1.3 Aim and Organization of Monograph.- Chapter 2 Perturbation-based Privacy Preservation.- 2.1 Classical Privacy Notions.- 2.1.1 Differentially Privacy.- 2.1.2 Identifiability.- 2.1.3 Mutual-Information Privacy.- 2.2 Relations between Privacy Notions.- 2.2.1 Differentially Privacy vs. Identifiability.- 2.2.2 Differentially Privacy vs. Mutual-Information Privacy.- 2.2.3 Identifiability vs. Mutual-Information Privacy.- 2.3 Summary.- Chapter 3 Semantic-Aware Trajectory Privacy Preservation in Crowdsensing.- 3.1 Problem Statement and Basic Concepts.- 3.1.1 Problem Statement.- 3.1.2 Basic Concepts.- 3.2 Privacy and Utility Metrics.- 3.2.1 Data Privacy Metric.- 3.2.2 Semantic Privacy Metric.- 3.2.3 Semantic-Aware Trajectory Utility Metric.- 3.3 Semantic-Aware Privacy Mapping Mechanism.- 3.3.1 Constructing Optimization Model.- 3.3.2 Solving Optimization Model.- 3.3.3 Computational Complexity.- 3.4 Privacy Analysis.- 3.5 Performance Evaluation.- 3.5.1 Simulation Settings.- 3.5.2 Simulation Results.- 3.6 Summary and Further Reading.- Chapter 4 Pricing-Aware Location Privacy Preservation in Crowdsensing.- 4.1 Problem Statement and Basic Concepts.- 4.1.1 Problem Statement.- 4.1.2 Basic Concepts.- 4.2 Utility Loss Metrics 4 4.2.1 Adaptive Supply and Demand Aware Grid.- 4.2.2 Dynamic Pricing Utility Metric.- 4.2.3 Ride-Matching Utility Metric.- 4.3 Pricing-Aware Privacy Mapping Mechanism.- 4.3.1 Constructing Optimization Model.- 4.3.2 Solving Optimization Model.- 4.3.3 Computational Complexity.- 4.4 Privacy Analysis.- 4.5 Performance Evaluation.- 4.5.1 Simulation Settings.- 4.5.2 Simulation Results.- 4.6 Summary and Further Reading.- Chapter 5 Data Poisoning Attacks and Defenses to LDP-based Crowdsensing.- 5.1 Problem Statement and Basic Concepts.- 5.1.1 Problem Statement.- 5.1.2 Basic Concepts.- 5.2 Data Poisoning Attacks Hidden behind the LDP Noise.- 5.2.1 LDP-based Privacy-Preserving Truth Discovery Methods.- 5.2.2 Formulating Optimal Data Poisoning Attacks.- 5.2.3 Finding Optimal Data Poisoning Attacks.- 5.3 Countermeasures: Designing Optimal Defenses.- 5.3.1 Formulating Optimal Countermeasures.- 5.3.2 Finding Optimal Countermeasures.- 5.4 Computational Complexity and Limitations of Attacks and Defenses.- 5.4.1 Computational Complexity of Attacks and Defenses.- 5.4.2 Limitations of Attacks and Defenses.- 5.5 Performance Evaluation.- 5.5.1 Simulation Settings.- 5.5.2 Simulation Results.- 5.6 Summary and Further Reading.- Chapter 6 Data Poisoning Attacks and Defenses to CDP-based Crowdsensing.- 6.1 Problem Statement and Basic Concepts.- 6.1.1 Problem Statement.- 6.1.2 Basic Concepts.- 6.2 Formulating Game Model between Attacks and Defenses.- 6.2.1 Zero-Sum Stackelberg Game.- 6.2.2 Unveiling the Normal Behavior of Workers.- 6.3 Finding Optimal Data Poisoning Attacks and Defenses.- 6.3.1 Defense Strategy for Defenders.- 6.3.2 Attack Strategy for Attackers.- 6.3.3 Local Minimax Point of Defenders-Attackers Interaction.- 6.4 Computational Complexity and Limitations of Attacks and Defenses.- 6.4.1 Computational Complexity of Attacks and defenses.- 6.4.2 Limitations of Attacks and Defenses 5 6.5 Performance Evaluation.- 6.5.1 Simulation Settings.- 6.5.2 Simulation Results.- 6.6 Summary and Further Reading.- Chapter 7 Conclusion and Future Works.- 7.1 Conclusion.- 7.2 Future Works.