Longxiang Gao - Privacy-Preserving in Edge Computing (Wireless Networks)

The purpose of Springers Wireless Networks book series is to establish the state of the art and set the course for future research and development in wireless communication networks. The scope of this series includes not only all aspects of wireless networks (including cellular networks, WiFi, sensor networks, and vehicular networks), but related areas such as cloud computing and big data. The series serves as a central source of references for wireless networks research and development. It aims to publish thorough and cohesive overviews on specific topics in wireless networks, as well as works that are larger in scope than survey articles and that contain more detailed background information. The series also provides coverage of advanced and timely topics worthy of monographs, contributed volumes, textbooks and handbooks.

Indexing: Wireless Networks is indexed in EBSCO databases and DPLB

More information about this series at http://www.springer.com/series/14180

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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 summarizes this monograph and proposes future research directions.

In summary, this book introduces the following techniques in edge computing: (1) describes an MDP-based privacy-preserving model to solve context-aware data privacy in the hierarchical edge computing paradigm; (2) describes an SDN-based clustering method to solve the location-aware privacy problems in edge computing; and (3) describes a novel blockchain-based decentralized privacy-preserving scheme in edge computing. These techniques enable the rapid development of privacy preserving in edge computing.