Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases by Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang
Requirements: .PDF reader, 17,4 MB
Overview: 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.
Genre: Non-Fiction > Tech & Devices
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