Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases (SpringerBriefs in Computer Science) free ebook download.

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Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases (SpringerBriefs in Computer Science) free ebook download

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Author(s):	Youyang Qu

Date:	2021	Format:	7z	Language:	English	ISBN/ASIN:	9811917965
Pages:	130	OCR:		Quality:		ISBN13:
Uploader:	alex21s	Upload Date:	5/6/2022 8:22:26 PM

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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.

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3118862	Book	https://nitro.download/view/36C4C3B0BFA0...		0.00	alex21s

Tags: Approaches Cases Comprehensive Computer Learning Machine Preservation Privacy Science SpringerBriefs

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