Ren Ping Liu – författare
1 811 kr
Skickas inom 10-15 vardagar
728 kr
Skickas inom 10-15 vardagar
1 325 kr
Skickas inom 10-15 vardagar
728 kr
Skickas inom 10-15 vardagar
844 kr
Läs direkt efter köp
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.
It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.
This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
844 kr
Läs direkt efter köp
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.
It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.
This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
776 kr
Läs direkt efter köp
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 privacyThis 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.
776 kr
Läs direkt efter köp
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 privacyThis 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.