Youyang Qu's Books

Privacy Preservation in IoT: Machine Learning Approaches

A Comprehensive Survey and Use Cases

By: Youyang Qu,Longxiang Gao,Shui Yu,Yong Xiang

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Privacy Preservation in IoT: Machine Learning Approaches

A Comprehensive Survey and Use Cases

By: Youyang Qu,Longxiang Gao,Shui Yu,Yong Xiang

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.

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Page Count

127

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Springer Nature

Published Date

ISBN 10

9811917973

ISBN 13

9789811917974

Personalized Privacy Protection in Big Data

By: Youyang Qu,Mohammad Reza Nosouhi,Lei Cui,Shui Yu

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Personalized Privacy Protection in Big Data

By: Youyang Qu,Mohammad Reza Nosouhi,Lei Cui,Shui Yu

This book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic.In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets. The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.

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Page Count

148

Publisher

Springer Nature

Published Date

ISBN 10

9811637504

ISBN 13

9789811637506

Privacy-Preserving in Edge Computing

By: Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang

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Privacy-Preserving in Edge Computing

By: Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang

With the rapid development of big data, it is necessary to transfer the massive data generated by end devices to the cloud under the traditional cloud computing model. However, the delays caused by massive data transmission no longer meet the requirements of various real-time mobile services. Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm. By extending the functions of the cloud to the edge of the network, edge computing provides effective data access control, computation, processing and storage for end devices. Furthermore, edge computing optimizes the seamless connection from the cloud to devices, which is considered the foundation for realizing the interconnection of everything. However, due to the open features of edge computing, such as content awareness, real-time computing and parallel processing, the existing problems of privacy in the edge computing environment have become more prominent. The access to multiple categories and large numbers of devices in edge computing also creates new privacy issues. In this book, we discuss on the research background and current research process of privacy protection in edge computing. In the first chapter, the state-of-the-art research of edge computing are reviewed. The second chapter discusses the data privacy issue and attack models in edge computing. Three categories of privacy preserving schemes will be further introduced in the following chapters. Chapter three introduces the context-aware privacy preserving scheme. Chapter four further introduces a location-aware differential privacy preserving scheme. Chapter five presents a new blockchain based decentralized privacy preserving in edge computing. Chapter six summarize this monograph and propose future research directions. In summary, this book introduces the following techniques in edge computing: 1) describe an MDP-based privacy-preserving model to solve context-aware data privacy in the hierarchical edge computing paradigm; 2) describe a SDN based clustering methods to solve the location-aware privacy problems in edge computing; 3) describe a novel blockchain based decentralized privacy-preserving scheme in edge computing. These techniques enable the rapid development of privacy-preserving in edge computing.

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113

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Springer Nature

Published Date

ISBN 10

9811621993

ISBN 13

9789811621994

Privacy Preservation in IoT: Machine Learning Approaches

A Comprehensive Survey and Use Cases

By: Youyang Qu,Longxiang Gao,Shui Yu,Yong Xiang

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Privacy Preservation in IoT: Machine Learning Approaches

A Comprehensive Survey and Use Cases

By: Youyang Qu,Longxiang Gao,Shui Yu,Yong Xiang

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

127

Publisher

Springer Nature

Published Date

ISBN 10

9811917973

ISBN 13

9789811917974

Privacy-Preserving in Edge Computing

By: Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang

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Privacy-Preserving in Edge Computing

By: Longxiang Gao,Tom H. Luan,Bruce Gu,Youyang Qu,Yong Xiang

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

113

Publisher

Springer Nature

Published Date

ISBN 10

9811621993

ISBN 13

9789811621994

Book's by Youyang Qu

Privacy Preservation in IoT: Machine Learning Approaches

Privacy Preservation in IoT: Machine Learning Approaches

Personalized Privacy Protection in Big Data

Personalized Privacy Protection in Big Data

Privacy-Preserving in Edge Computing

Privacy-Preserving in Edge Computing

Privacy Preservation in IoT: Machine Learning Approaches

Privacy Preservation in IoT: Machine Learning Approaches

Privacy-Preserving in Edge Computing

Privacy-Preserving in Edge Computing

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