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Privacy Preservation in IoT: Machine Learning Approaches

- A Comprehensive Survey and Use Cases
Af: Longxiang Gao, Shui Yu, Yong Xiang, Youyang Qu Engelsk Paperback

Privacy Preservation in IoT: Machine Learning Approaches

- A Comprehensive Survey and Use Cases
Af: Longxiang Gao, Shui Yu, Yong Xiang, Youyang Qu Engelsk Paperback
Tjek vores konkurrenters priser

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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 119
ISBN-13: 9789811917967
Indbinding: Paperback
Udgave:
ISBN-10: 9811917965
Kategori: Machine learning
Udg. Dato: 28 apr 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 28 apr 2022
Forfatter(e): Longxiang Gao, Shui Yu, Yong Xiang, Youyang Qu
Forfatter(e) Longxiang Gao, Shui Yu, Yong Xiang, Youyang Qu

Kategori Machine learning

ISBN-13 9789811917967

Sprog Engelsk

Indbinding Paperback

Sider 119

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 28 apr 2022

Oplagsdato 28 apr 2022

Forlag Springer Verlag, Singapore

Kategori sammenhænge