Youyang (63 results)

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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

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Paperback. Condition: Brand New. 179 pages. 9.25x6.10x0.39 inches. In Stock.

Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed post-conference proceedings of the 17th EAI International Conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TridentCom 2022, which was held in Melbourne, Australia, in November 23-25,2022. The 11 full papers were selected from 30 submissions and deal the emerging technologies of big data, cyber-physical systems and computer communications. The papers are grouped in thematical sessions on network security; network communication; network services; mobile and ad hoc networks; blockchain; machine learning.…

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Taschenbuch. Condition: Neu. Privacy Preservation in IoT: Machine Learning Approaches | A Comprehensive Survey and Use Cases | Youyang Qu (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | xi | Englisch | 2022 | Springer | EAN 9789811917967 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Condition: Neu. Tools for Design, Implementation and Verification of Emerging Information Technologies | 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings | Shui Yu (u. a.) | Taschenbuch | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering | x | Englisch | 2023 | Springer | EAN 9783031334573 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Taschenbuch. Condition: Neu. Personalized Privacy Protection in Big Data | Youyang Qu (u. a.) | Taschenbuch | Data Analytics | xi | Englisch | 2022 | Springer | EAN 9789811637520 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner.The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions.Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.…

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