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Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Privacy preservation in IoTby Youyang Qu ... [et al.].
Reminder of title:	machine learning approaches : a comprehensive survey and use cases /
other author:	Qu, Youyang.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xi, 119 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Internet of thingsSecurity measures.
Online resource:	https://doi.org/10.1007/978-981-19-1797-4
ISBN:	9789811917974$q(electronic bk.)

Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /
Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /[electronic resource] :by Youyang Qu ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xi, 119 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Chapter 1 Introduction -- Chapter 2 Current Methods of Privacy Protection in IoTs -- Chapter 3 Decentralized Privacy Protection of IoTs using Blockchain-Enabled Federated Learning -- Chapter 4 Personalized Privacy Protection of IoTs using GAN-Enhanced Differential Privacy -- Chapter 5 Hybrid Privacy Protection of IoT using Reinforcement Learning -- Chapter 6 Future Directions -- Chapter 7 Summary and Outlook.

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.

ISBN: 9789811917974$q(electronic bk.)

Standard No.: 10.1007/978-981-19-1797-4doiSubjects--Topical Terms:

770009
Internet of things
--Security measures.

LC Class. No.: TK5105.8857

Dewey Class. No.: 004.678

Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /
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520 $a 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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