Privacy-Preserving in Mobile Crowdsensing

Mobile crowdsensing is a new sensing paradigm that utilizes the intelligence of a crowd of individuals to collect data for mobile purposes. This "sensing as a service" elaborates our knowledge of the physical world by opening up a new door of data collection and analysis. However, privacy issues urgently need to be solved. This book discusses the research background and current research process of privacy protection in mobile crowdsensing, including techniques such as randomizable matrix-based task-matching methods, multi-clouds randomizable matrix-based task-matching methods, and privacy-preserving truth discovery methods. These techniques are vital to the rapid development of privacy-preserving in mobile crowdsensing.

Format: Hardback
Length: 197 pages
Publication date: 25 March 2023
Publisher: Springer Verlag, Singapore

Mobile crowdsensing is a revolutionary sensing paradigm that harnesses the collective intelligence of a group of individuals to collect data for various mobile applications through their portable devices, such as smartphones and wearable devices. This approach involves incentivizing individuals to participate in data collection tasks, often released by data requesters. By leveraging the power of crowdsourcing, mobile crowdsensing expands our understanding of the physical world by providing a new avenue for data collection and analysis.

However, with the increasing adoption of mobile crowdsensing, privacy concerns have become increasingly urgent. In response, this book aims to delve into the research background and current research processes related to privacy protection in mobile crowdsensing.

In the first chapter, we provide an overview of the background, system model, and threat model of mobile crowdsensing. We explore the fundamental principles and challenges associated with this emerging field.

The second chapter focuses on the current techniques employed to safeguard user privacy in mobile crowdsensing. We discuss various approaches, such as anonymization, encryption, and differential privacy, that are used to protect sensitive information and maintain user anonymity.

Chapter three introduces a privacy-preserving content-based task allocation scheme. We discuss how this scheme ensures task privacy while allowing efficient content-based task matching. We explore the use of randomization techniques and matrix-based methods to protect task identities and prevent unauthorized access.

Chapter four delves into the privacy-preserving location-based task scheme. We discuss how this scheme protects location privacy by leveraging randomization techniques and location-based services. We explore the use of location-based encryption and differential privacy to ensure that sensitive location data remains confidential.

Chapter five presents a privacy-preserving truth discovery scheme with truth transparency. We discuss how this scheme enables efficient and secure truth discovery while maintaining transparency and accountability. We explore the use of privacy-preserving data aggregation and truth verification techniques to protect user privacy.

Chapter six proposes a privacy-preserving truth discovery scheme with truth hiding. We discuss how this scheme enables truth discovery while concealing sensitive information. We explore the use of cryptographic techniques and data perturbation methods to protect user privacy.

Chapter seven summarizes the key findings and conclusions of this monograph. We propose future research directions and areas of exploration in mobile crowdsensing privacy protection.

In conclusion, this book provides a comprehensive overview of the research background and current research processes related to privacy protection in mobile crowdsensing. By introducing various techniques and schemes, we aim to contribute to the development of a more secure and privacy-preserving mobile crowdsensing ecosystem.

Weight: 494g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811983146
Edition number: 1st ed. 2023