LEADER 04105nam 22005895 450 001 9910865266903321 005 20250807153306.0 010 $a3-031-58013-3 024 7 $a10.1007/978-3-031-58013-0 035 $a(MiAaPQ)EBC31359069 035 $a(Au-PeEL)EBL31359069 035 $a(CKB)32200393700041 035 $a(DE-He213)978-3-031-58013-0 035 $a(EXLCZ)9932200393700041 100 $a20240531d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrivacy Preservation in Distributed Systems $eAlgorithms and Applications /$fby Guanglin Zhang, Ping Zhao, Anqi Zhang 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (266 pages) 225 1 $aSignals and Communication Technology,$x1860-4870 311 08$a3-031-58012-5 327 $aIntroduction -- Part I Privacy lssues in Data Aggregation -- LocMIA: Membership Inference Attacks against Aggregated Location Data -- Synthesizing Privacy Preserving Traces: Enhancing Plausibility with Social Networks -- DAML: Practical Secure Protocol for Data Aggregation based Machine Learning -- Enhancing Privacy Preservation in Speech Data Publishing -- Part II Privacy Issues in Indoor Localization -- Lightweight Privacy-Preserving Scheme in WiFi Fingerprint-Based Indoor Localization -- P3LOC: A Privacy-Preserving Paradigm-Driven framework for Indoor Localization -- Preserving Privacy in WiFi Localization with Plausible Dummy Locations -- Part III Privacy-Preserving Offloading in MEC -- Deep Reinforcement Learning-based Joint Optimization of Delay and Privacy in Multiple-User MEC Systems -- Load Balancing for Energy-Harvesting Mobile Edge Computing -- Learning-based Joint Optimization of Energy-Delay and Privacyin Multiple-User Edge-Cloud Collaboration MEC Systems. 330 $aThis book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-Preserving Offloading in MEC. In Part 1, the book proposes LocMIA, which shifts from membership inference attacks against aggregated location data to a binary classification problem, synthesizing privacy preserving traces by enhancing the plausibility of synthetic traces with social networks. In Part 2, the book highlights Indoor Localization to propose a lightweight scheme that can protect both location privacy and data privacy of LS. In Part 3, it investigates the tradeoff between computation rate and privacy protection for task offloading a multi-user MEC system, and verifies that the proposed load balancing strategy improves the computing service capability of the MEC system. In summary, all the algorithms discussed in this book are of great significance in demonstrating the importance of privacy. Addresses privacy concerns related to Data Aggregation, Indoor Localization, and Mobile Edge Computing; Introduces innovative solutions and algorithms to tackle privacy challenges; Offers readers a forward-looking perspective into future developments and challenges in privacy research. 410 0$aSignals and Communication Technology,$x1860-4870 606 $aTelecommunication 606 $aComputational intelligence 606 $aMachine learning 606 $aCommunications Engineering, Networks 606 $aComputational Intelligence 606 $aMachine Learning 615 0$aTelecommunication. 615 0$aComputational intelligence. 615 0$aMachine learning. 615 14$aCommunications Engineering, Networks. 615 24$aComputational Intelligence. 615 24$aMachine Learning. 676 $a621,382 700 $aZhang$b Guanglin$01742657 701 $aZhao$b Ping$01350298 701 $aZhang$b Anqi$01059749 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910865266903321 996 $aPrivacy Preservation in Distributed Systems$94169367 997 $aUNINA