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 ...
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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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Add this copy of Privacy Preservation in IoT: Machine Learning to cart. $56.20, like new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2022 by Springer Verlag, Singapore.
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Fine. Contains: Illustrations, black & white, Illustrations, color. SpringerBriefs in Computer Science . XI, 119 p. 39 illus., 36 illus. in color. Intended for professional and scholarly audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Privacy Preservation in IoT: Machine Learning to cart. $56.34, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2022 by Springer Verlag, Singapore.
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Seller's Description:
New. Contains: Illustrations, black & white, Illustrations, color. SpringerBriefs in Computer Science . XI, 119 p. 39 illus., 36 illus. in color. Intended for professional and scholarly audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Privacy Preservation in IoT: Machine Learning to cart. $56.35, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2022 by Springer Verlag, Singapore.
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Seller's Description:
New. Print on demand Contains: Illustrations, black & white, Illustrations, color. SpringerBriefs in Computer Science . XI, 119 p. 39 illus., 36 illus. in color. Intended for professional and scholarly audience.
