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Privacy Preservation in IoT: Machine Learning Approaches (Paperback)
Book 8 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811917965 ISBN 13: 9789811917967
First Edition
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Tools for Design, Implementation and Verification of Emerging Information Technologies: 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings
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Privacy Preservation in Iot - Machine Learning Approaches : A Comprehensive Survey and Use Cases
Book 8 of 83: SpringerBriefs in Computer ScienceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases (SpringerBriefs in Computer Science)
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Privacy Preservation In Iot: Machine Lea
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Privacy Preservation in Iot - Machine Learning Approaches : A Comprehensive Survey and Use Cases
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Tools for Design, Implementation and Verification of Emerging Information Technologies: 17th EAI International Conference, TridentCom 2022, Melbourne, ... and Telecommunications Engineering, 489)
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Personalized Privacy Protection in Big Data (Data Analytics)
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Personalized Privacy Protection in Big Data (Data Analytics)
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Personalized Privacy Protection in Big Data
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Personalized Privacy Protection In Big D
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Personalized Privacy Protection in Big Data
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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Tools for Design, Implementation and Verification of Emerging Information Technologies: 17th Eai International Conference, Tridentcom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings
Language: English
Published by Springer-Nature New York Inc, 2023
ISBN 10: 3031334574 ISBN 13: 9783031334573
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Add to basketPaperback. Condition: Brand New. 179 pages. 9.25x6.10x0.39 inches. In Stock.
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Personalized Privacy Protection in Big Data
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2021
ISBN 10: 9811637490 ISBN 13: 9789811637490
Condition: New.
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Personalized Privacy Protection in Big Data
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811637520 ISBN 13: 9789811637520
Condition: New.
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Privacy Preservation in IoT: Machine Learning Approaches (Paperback)
Book 8 of 83: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811917965 ISBN 13: 9789811917967
First Edition
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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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 | 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.
