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Privacy preservation in distributed systemsalgorithms and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Privacy preservation in distributed systemsby Guanglin Zhang, Ping Zhao, Anqi Zhang.
其他題名:	algorithms and applications /
作者:	Zhang, Guanglin.
其他作者:	Zhao, Ping.
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xiv, 256 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer networksSecurity measures.
電子資源:	https://doi.org/10.1007/978-3-031-58013-0
ISBN:	9783031580130$q(electronic bk.)

Privacy preservation in distributed systemsalgorithms and applications /
Zhang, Guanglin.

Privacy preservation in distributed systemsalgorithms and applications /[electronic resource] :by Guanglin Zhang, Ping Zhao, Anqi Zhang. - Cham :Springer Nature Switzerland :2024. - xiv, 256 p. :ill. (some col.), digital ;24 cm. - Signals and communication technology,1860-4870. - Signals and communication technology..

Introduction -- 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.

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.

ISBN: 9783031580130$q(electronic bk.)

Standard No.: 10.1007/978-3-031-58013-0doiSubjects--Topical Terms:

185597
Computer networks
--Security measures.

LC Class. No.: TK5105.59

Dewey Class. No.: 005.8

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505 0 $a Introduction -- 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 $a 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.
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