Perturbation Based Privacy in Crowdsensing

Inbunden, Engelska, 2025

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The authors first present an explicit overview of crowdsensing systems and privacy challenges and briefly discuss how the noise added by perturbation-based privacy-preserving techniques could inevitably degrade data quality and facilitate the success of data poisoning attacks on crowdsensing.

Produktinformation

Utgivningsdatum2025-07-22
Mått155 x 235 x 17 mm
Vikt457 g
FormatInbunden
SpråkEngelska
SerieWireless Networks
Antal sidor179
FörlagSpringer International Publishing AG
ISBN9783031950513

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Dr. Zhirun Zheng received his B.S. in the School of Mathematics and Statistics, Henan University of Science and Technology, China, in 2017, and received his Ph.D. degree in the School of Mathematics and Computational Science, Xiangtan University, China, in 2024. From December 2022 to December 2023, he visited the Broadband Communications Research (BBCR) Lab, Department of Electrical and Computer Engineering, University of Waterloo, Canada. Since October 2024, he is a Postdoctoral Fellow in the Department of Artificial Intelligence, Ajou University, South Korea. His research interests are in the areas of security and privacy in Crowdsensing systems, artificial intelligence, and mobile networks. Dr. Zheng serves as TPC members of many conferences, including GLOBECOM and WCSP.Dr. Zhetao Li is a professor in the College of Information Science and Technology, Jinan University. He received the B.Eng. degree in electrical information engineering from Xiangtan University in 2002, the M.Eng. degree in pattern recognition and intelligent systems from Beihang University in 2005, and the Ph.D. degree in computer application technology from Hunan University in 2010. From Dec 2013 to Dec 2014, he was a postdoc in wireless networks at Stony Brook University. He is a member of IEEE and CCF. Dr. Xuemin (Sherman) Shen received the Ph.D. degree in electrical engineering from Rutgers University, New Brunswick, NJ, USA, in 1990. He is a University Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Canada. His research focuses on network resource management, wireless network security, Internet of Things, 5G and beyond, and vehicular ad hoc and sensor networks. Dr. Shen is a registered Professional Engineer of Ontario, Canada, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, a Chinese Academy of Engineering Foreign Member, and a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society. Dr. Shen received the R.A. Fessenden Award in 2019 from IEEE, Canada, Award of Merit from the Federation of Chinese Canadian Professionals (Ontario) in 2019, James Evans Avant Garde Award in 2018 from the IEEE Vehicular Technology Society, Joseph LoCicero Award in 2015 and Education Award in 2017 from the IEEE Communications Society, and Technical Recognition Award from Wireless Communications Technical Committee (2019) and AHSN Technical Committee (2013). He has also received the Excellent Graduate Supervision Award in 2006 from the University of Waterloo and the Premier’s Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. He served as the Technical Program Committee Chair/Co-Chair for IEEE Globecom’16, IEEE Infocom’14, IEEE VTC’10 Fall, IEEE Globecom’07, and the Chair for the IEEE Communications Society Technical Committee on Wireless Communications. Dr. Shen is the President Elect of the IEEE Communications Society. He was the Vice President for Technical & Educational Activities, Vice President for Publications, Member-at-Large on the Board of Governors, Chair of the Distinguished Lecturer Selection Committee, Member of IEEE Fellow Selection Committee of the ComSoc. Dr. Shen served as the Editor-in-Chief of the IEEE IoT Journal, IEEE Network, and IET Communications.

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.