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Privacy Preservation in IoT: Machine Learning Approaches - A Comprehensive Survey and Use Cases (Paperback, 1st ed. 2022)
Loot Price: R1,519
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Privacy Preservation in IoT: Machine Learning Approaches - A Comprehensive Survey and Use Cases (Paperback, 1st ed. 2022)
Series: SpringerBriefs in Computer Science
Expected to ship within 12 - 19 working days
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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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