TY  - BOOK
AU  - Qu,Youyang
AU  - Gao,Longxiang
AU  - Yu,Shui
AU  - Xiang,Yong
TI  - Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases 
T2  - SpringerBriefs in Computer Science,
SN  - 9789811917974
U1  - 005.8 23
PY  - 2022///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Data protection-Law and legislation
KW  - Machine learning
KW  - Internet of things
KW  - Big data
KW  - Data mining
KW  - Artificial intelligence-Data processing
KW  - Privacy
KW  - Machine Learning
KW  - Internet of Things
KW  - Big Data
KW  - Data Mining and Knowledge Discovery
KW  - Data Science
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-981-19-1797-4
ER  -