Mobile crowdsensing is a new sensing paradigm that utilizes the intelligence of a crowd of individuals to collect data for mobile purposes by using their portable devices, such as smartphones and wearable devices. Commonly, individuals are incentivized to collect data to fulfill a crowdsensing task released by a data requester. This “sensing as a service” elaborates our knowledge of the physical world by opening up a new door of data collection and analysis. However, with the expansion of mobile crowdsensing, privacy issues urgently need to be solved.
In this book, we discuss the research background and current research process of privacy protection in mobile crowdsensing. In the first chapter, the background, system model, and threat model of mobile crowdsensing are introduced. The second chapter discusses the current techniques to protect user privacy in mobile crowdsensing. Chapter three introduces the privacy-preserving content-based task allocation scheme. Chapter four further introduces the privacy-preserving location-based task scheme. Chapter five presents the scheme of privacy-preserving truth discovery with truth transparency. Chapter six proposes the scheme of privacy-preserving truth discovery with truth hiding. Chapter seven summarizes this monograph and proposes future research directions.
In summary, this book introduces the following techniques in mobile crowdsensing: 1) describe a randomizable matrix-based task-matching method to protect task privacy and enable secure content-based task allocation; 2) describe a multi-clouds randomizable matrix-based task-matching method to protect location privacy and enable secure arbitrary range queries; and 3) describe privacy-preserving truth discovery methods to support efficient and secure truth discovery. These techniques are vital to the rapid development of privacy-preserving in mobile crowdsensing.
Author(s): Chuan Zhang, Tong Wu, Youqi Li, Liehuang Zhu
Publisher: Springer
Year: 2023
Language: English
Pages: 204
City: Singapore
Foreword
Preface
Acknowledgements
Contents
Acronyms
Part I Overview and Basic Concept of Mobile Crowdsensing Technology
1 Introduction
1.1 Background
1.2 Mobile Crowdsensing
1.2.1 System Model of MCS
1.2.2 Security Model of MCS
1.3 The State of the Art and Trend of Privacy-Preserving
1.3.1 Privacy-Preserving Task Allocation
1.3.1.1 Privacy-Preserving Content-Based Task Allocation
1.3.1.2 Privacy-Preserving Location-Based Task Allocation
1.3.2 Privacy-Preserving Truth Discovery
1.4 Organization of the Book
References
2 Overview of MCS Technology
2.1 Preliminary of Privacy-Preserving Data Collection Techniques
2.1.1 Polynomial Function
2.1.2 Secure Hash Function
2.1.3 Searchable Encryption
2.1.4 Asymmetric Scalar-Product-Preserving Encryption
2.1.5 Polynomial Fitting
2.2 Preliminary of Privacy-Preserving Data Analysis Techniques
2.2.1 Truth Discovery
2.2.2 Public-Key Cryptosystem Supporting Distributed Decryption
References
Part II Privacy-Preserving Task Allocation
3 Privacy-Preserving Content-Based Task Allocation
3.1 Introduction
3.1.1 Overview
3.1.2 Related Works
3.1.3 Preliminary
3.1.3.1 Polynomial Function
3.2 Architecture Overview
3.2.1 System Model
3.2.2 Security Model
3.2.3 Design Goals
3.3 Detailed Design
3.3.1 Proposed PPTA Scheme
3.3.2 Correctness Analysis
3.3.3 Extension and Discussion
3.3.3.1 Privacy-Preserving Conjunctive Task Allocation
3.3.3.2 Privacy-Preserving Top-z Task Allocation
3.3.3.3 Privacy-Preserving Task Allocation with Access Control
3.3.3.4 Privacy-Preserving Task Recovery
3.4 Security Analysis
3.4.1 Security Under Passive Attack
3.4.2 Security Under Active Attack
3.5 Performance Evaluation and Analysis
3.5.1 Theoretical Analysis
3.5.2 Performance Evaluation
3.6 Summary
References
4 Privacy-Preserving Location-Based Task Allocation
4.1 Introduction
4.1.1 Overview
4.1.2 Related Works
4.1.3 Preliminary
4.1.3.1 Polynomial Fitting
4.2 Architecture Overview
4.2.1 System Model
4.2.2 Security Model
4.2.3 Design Goals
4.3 Detailed Design
4.3.1 Proposed GPTA-L Scheme
4.3.2 Correctness of GPTA-L
4.3.3 Proposed GPTA-F Scheme
4.3.4 Correctness of GPTA-F
4.4 Security Analysis
4.4.1 Security Analysis of the Data Encryption Phase
4.4.1.1 Security Under Passive Attack
4.4.1.2 Security Under Active Attack
4.4.2 Security Analysis of the Data Re-encryption Phase
4.4.3 Security Analysis of the Data Query Phase
4.5 Performance Evaluation and Analysis
4.5.1 Query Accuracy
4.5.2 Theoretical Analysis
4.5.3 Performance Evaluation
4.6 Summary
References
Part III Privacy-Preserving Truth Discovery
5 Privacy-Preserving Truth Discovery with Truth Transparency
5.1 Introduction
5.1.1 Overview
5.1.2 Related Works
5.1.3 Preliminary
5.1.3.1 Polynomial Function
5.1.3.2 Searchable Encryption
5.1.3.3 Truth Discovery
5.1.3.4 Public-Key Cryptosystem Supporting Distributed Decryption
5.2 Architecture Overview
5.2.1 System Model
5.2.2 Security Model
5.2.3 Design Goals
5.3 Detailed Design
5.3.1 RPTD-I: A Scheme for User Participation in the Iterative Process of Truth Discovery
5.3.1.1 System Initialization
5.3.1.2 The Iterative Process
5.3.2 RPTD-II: A Scheme Where Users Do Not Need to Participate in the Truth Discovery Iterative Process
5.3.2.1 System Initialization
5.3.2.2 The Iterative Process
5.4 Security Analysis
5.4.1 RPTD-I Security and Privacy Analysis
5.4.2 RPTD-II Security and Privacy Analysis
5.5 Performance Evaluation and Analysis
5.5.1 Theoretical Analysis
5.5.1.1 Theoretical Analysis of RPTD-I
5.5.1.2 Theoretical Analysis of RPTD-II
5.5.2 Performance Analysis
5.5.2.1 Experiments Based on Real Crowdsensing Scenarios
5.5.2.2 Experiments Based on Simulated Crowdsensing Scenarios
5.6 Summary
References
6 Privacy-Preserving Truth Discovery with Truth Hiding
6.1 Introduction
6.1.1 Overview
6.1.2 Related Works
6.1.3 Preliminary
6.1.3.1 Polynomial Function
6.1.3.2 Searchable Encryption
6.1.3.3 Truth Discovery
6.1.3.4 Public-Key Cryptosystem Supporting Distributed Decryption
6.2 Architecture Overview
6.2.1 System Model
6.2.2 Security Model
6.2.3 Design Goals
6.3 Detailed Design
6.3.1 Initialization
6.3.2 Iteration
6.4 Security Analysis
6.5 Performance Evaluation and Analysis
6.5.1 Computational Cost
6.5.1.1 Experiments Based on Real Mobile Crowdsensing Scenarios
6.5.1.2 Experiments Based on Simulated Mobile Crowdsensing Scenarios
6.5.2 Communication Overhead
6.6 Summary
References
7 Privacy-Preserving Truth Discovery with Task Hiding
7.1 Introduction
7.1.1 Overview
7.1.2 Related Works
7.1.3 Preliminary
7.1.3.1 Majority Voting
7.1.3.2 Truth Discovery
7.1.3.3 Secure kNN Computation
7.2 Architecture Overview
7.2.1 System Model
7.2.2 Security Model
7.2.3 Design Goals
7.3 Detailed Design
7.3.1 Setup
7.3.2 Mobile Crowdsensing Data Submission
7.3.3 Privacy-Preserving Truth Discovery
7.4 Security Analysis
7.5 Performance Evaluation and Analysis
7.5.1 Accuracy
7.5.2 Convergence
7.5.3 Computational Cost
7.5.4 Communication Overhead
7.6 Summary
References
Part IV Summary and Future Research Directions
8 Summary
8.1 Conclusion
8.2 Outlook
