Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases (SpringerBriefs in Computer Science) 1st ed. 2022 Edition
Author: Youyang Qu (Author), Longxiang Gao (Author), Shui Yu (Author), Yong Xiang (Author)
Publisher : Springer; 1st ed. 2022 edition (April 28, 2022)
Language : English
Paperback : 130 pages
ISBN-10 : 9811917965
ISBN-13 : 9789811917967
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 Author: 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 Author: 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.