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This book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods.
List of contents
1. Introduction 2. Location-based Services and Location Privacy Protection 3. Dynamic Statistical Publishing and Privacy Protection of Location-based Big Data via Adaptive Sampling and Grid Clustering 4. Localized Location Privacy Protection based on Optimized Random Response 5. Achieving Location Privacy Protection via Geo-indistinguishability and Location Semantics 6. Conclusion
About the author
Yan Yan is a Professor of the School of Computer and Communication, Lanzhou University of Technology, China. Her research interests include, but are not limited to, privacy preserving data collection, privacy preserving data publishing, blockchain transaction privacy protection, and multimedia information security.
Adnan Mahmood is a Lecturer in Computing-IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia. His research interests include, but are not limited to, the Internet of Things (primarily, the Internet of Vehicles), Trust Management, Software-Defined Networking, and the Next Generation Heterogeneous Wireless Networks.
Quan Z. Sheng is a Distinguished Professor and Head of School of Computing at Macquarie University, Australia. He is ranked by Microsoft Academic as one of the Most Impactful Authors in Services Computing (ranked Top 5 of All Time worldwide) and in the Web of Things (ranked Top 20 All Time).
Summary
This book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods.
