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Full Description
In an era where vehicular networks and Location-Based Services (LBS) are rapidly expanding, safeguarding location privacy has become a critical challenge. Privacy in Vehicular Networks delves into the complexities of protecting sensitive location data within the dynamic and decentralized environment of vehicular networks. This book stands out by addressing both the theoretical and practical aspects of location privacy, offering a thorough analysis of existing vulnerabilities and innovative solutions.
This book meticulously examines the interplay between location privacy and the operational necessities of road networks. It introduces a differential privacy framework tailored specifically for vehicular environments, ensuring robust protection against various types of privacy breaches. By integrating advanced detection algorithms and personalized obfuscation schemes, the book provides a multi-faceted approach to enhancing location privacy without compromising data utility.
The key features of this book can be summarized as follows:
Comprehensive Analysis: Detailed examination of location privacy requirements and existing preservation mechanisms
Innovative Solutions: Introduction of a Personalized Location Privacy-Preserving (PLPP) mechanism based on Road Network-Indistinguishability (RN-I)
Advanced Detection: Utilization of Convolutional Neural Networks (CNN) for detecting illegal trajectories and enhancing data integrity
Collective Security: Implementation of the Cloaking Region Obfuscation (CRO) mechanism to secure multiple vehicles in high-density road networks
Holistic Approach: Joint Trajectory Obfuscation and Pseudonym Swapping (JTOPS) mechanism to seamlessly integrate privacy preservation with traffic management
Future-Ready: Exploration of upcoming challenges and recommendations for future research in vehicular network privacy
This book is essential for researchers, practitioners, and policymakers in the fields of vehicular networks, data privacy, and cybersecurity. It provides valuable insights for anyone involved in the development and implementation of LBS, ensuring they are equipped with the knowledge to protect user privacy effectively.
Contents
Abstract. List of Figures. List of Tables. 1. Introduction. 2. Earlier Development of LPPM. 3. Personalized Location Privacy with RN-I. 4. Vehicle Trajectory Obfuscation and Detection. 5. Cloaking Region Obfuscation for RN-I and Location Privacy. 6. Cooperative Trajectory Privacy Protection. 7. Conclusion. Bibliography. Index.
