Description
Book SynopsisThis 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.
Table of Contents· Chapter 1: Introduction
o Privacy research landscape
o Machine learning driven privacy preservation overview
o Contribution of this monograph
o Outline of the monograph
· Chapter 2: Current Methods of Privacy Protection in IoTs
o Cryptography based methods
o Differential privacy methods
o Anonymity-based methods
o Clustering-based methods
· Chapter 3: Decentralized Privacy Protection of IoTs using Blockchain-Enabled Federated Learning
o Overview
o System Modelling
o Decentralized Privacy Protocols
o Blockchain-enabled Federated Learning
· Chapter 4: Personalized Privacy Protection of IoTs using GAN-Enhanced Differential Privacy
o Overview
o System Modelling
o Personalized Privacy
o GAN-Enhanced Differential Privacy
· Chapter 5: Hybrid Privacy Protection of IoT using Reinforcement Learning
o Overview
o System Modelling
o Hybrid Privacy
o Markov Decision Process and Reinforcement Learning
· Chapter 6: Future Directions
o Trade-off optimization
o Privacy preservation of digital twin
o Privacy-preserving federated learning
o Federated generative adversarial nets
· Chapter 7: Summary and Outlook
