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020 _a9783031580130
_9978-3-031-58013-0
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082 0 4 _a621.382
_223
100 1 _aZhang, Guanglin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aPrivacy Preservation in Distributed Systems
_h[electronic resource] :
_bAlgorithms and Applications /
_cby Guanglin Zhang, Ping Zhao, Anqi Zhang.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIV, 256 p. 93 illus., 92 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSignals and Communication Technology,
_x1860-4870
505 0 _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.
520 _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.
541 _fUABC ;
_cPerpetuidad
650 0 _aTelecommunication.
650 0 _aComputational intelligence.
650 0 _aMachine learning.
650 1 4 _aCommunications Engineering, Networks.
650 2 4 _aComputational Intelligence.
650 2 4 _aMachine Learning.
700 1 _aZhao, Ping.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhang, Anqi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031580123
776 0 8 _iPrinted edition:
_z9783031580147
776 0 8 _iPrinted edition:
_z9783031580154
830 0 _aSignals and Communication Technology,
_x1860-4870
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-58013-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c275185
_d275184