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Bücher Englische Bücher Wissen & Bildung Naturwissenschaft, Medizin, Informatik, Technik Informatik & EDV Informatik Privacy Preservation in IoT: Machine Learning Approaches
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Privacy Preservation in IoT: Machine Learning Approaches
Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang (Broschiert, Englisch)
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Beschreibung
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. 58,84 €
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Technische Daten
Erscheinungsdatum
28.04.2022
Sprache
Englisch
EAN
9789811917967
Herausgeber
Springer Singapore
Serien- oder Bandtitel
SpringerBriefs in Computer Science
Sonderedition
Nein
Autor
Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang
Seitenanzahl
119
Auflage
1
Einbandart
Broschiert
Hersteller: Springer Nature Customer Service Center GmbH, ProductSafety@springernature.com
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