Privacy preservation in IoT : machine learning approaches : a comprehensive survey and use cases / Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang.
2022
TK5105.8857
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Title
Privacy preservation in IoT : machine learning approaches : a comprehensive survey and use cases / Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang.
Author
ISBN
9789811917974 (electronic bk.)
9811917973 (electronic bk.)
9789811917967
9811917965
9811917973 (electronic bk.)
9789811917967
9811917965
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color).
Item Number
10.1007/978-981-19-1797-4 doi
Call Number
TK5105.8857
Dewey Decimal Classification
004.67/8
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed May 9, 2022).
Series
SpringerBriefs in computer science. 2191-5776
Available in Other Form
Print version: 9789811917967
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Table of Contents
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.
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.