This book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-Preserving Offloading in MEC. In Part 1, the book proposes LocMIA, which shifts from membership inference attacks against aggregated location data to a binary classification problem, synthesizing privacy preserving traces by enhancing the plausibility of synthetic traces with social networks. In Part 2, the book highlights Indoor Localization to propose a lightweight scheme that can protect both location privacy and data privacy of LS. In Part 3, it investigates the tradeoff between computation rate and privacy protection for task offloading a multi-user MEC system, and verifies that the proposed load balancing strategy improves the computing service capability of the MEC system. In summary, all the algorithms discussed in this book are of great significance in demonstrating the importance of privacy.

• Addresses privacy concerns related to Data Aggregation, Indoor Localization, and Mobile Edge Computing;
• Introduces innovative solutions and algorithms to tackle privacy challenges;
• Offers readers a forward-looking perspective into future developments and challenges in privacy research.

Autorentext

Guanglin Zhang received the B.S. degree in Applied Mathematics from Shandong Normal University, Jinan, China, in 2003, the M.S. degree in Operational Research and Cybernetics from Shanghai University, Shanghai, China, in 2006, and the Ph.D. degree in Information and Communication Engineering from Shanghai Jiao Tong University, Shanghai, in 2012. From 2013 to 2014, he was a Post-Doctoral Research Associate with the Institute of Network Coding, The Chinese University of Hong Kong. He joined Donghua University, as an Associate Professor, in 2014, and was promoted to Full Professor in 2017. From 2015 to 2021, he was the Department Chair of Communication Engineering. From 2020 to 2023, he was the Associate Dean of the College of Information Science and Technology, Donghua University. He is currently the Special Appointment Eastern Scholar Professor, the Director of the Office of Talent Affairs, and an Associate Director of the Department of Human Resources, Donghua University. His research interests include online algorithms, capacity scaling of wireless networks, vehicular networks, smart microgrids, and mobile edge computing. He serves as a Technical Program Committee Member for IEEE GLOBECOM 2016-2017, IEEE ICC 2014, 2015, and 2017, IEEE VTC 2017 Fall, IEEE/CIC ICCC 2014, WCSP 2014, APCC 2013, and WASA 2012. He serves as the Local Arrangement Chair for ACM TURC 2017 and the Vice TPC Co-Chair for ACM TURC 2018. He serves as an Editor on the Editorial Board for China Communications. He is an Associate Editor for the Journal of Computers and Electrical Engineering.

Ping Zhao received the Ph.D. degree from School of Electronic Information and Communications, Huazhong University of Science and Technology in 2018. Thereafter, she joined the faculty of Donghua University where she is currently an associate professor. Her research interests are in the area of wireless communication networking, mobile computing, and data security. She served a Guest Editor of International Distributed Sensor Network 2020, the Web Chair and Publication Chair of International Conference on Edge Computing and IoT: Systems, Management and Security 2020 and 2022, and the Vice Technical Program Co-Chairs of International Symposium on Computing and Artificial Intelligence 2023. She received the ACM Wuhan Doctoral Dissertation Award in 2019, and the Excellent Reviewer For IEEE Transactions on Network Science and Engineering in 2022.

Anqi Zhang received the Master degree from Donghua University in 2021. She is currently pursuing the Ph.D. degree in the College of Information Science and Technology, Donghua University. Her research interests include the area of wireless communication networking, attack and defense in machine learning and data privacy. Mainly focus on the privacy and robustness in federated learning.