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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 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.
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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Tools for Design, Implementation and Verification of Emerging Information Technologies - 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings (1st ed. 2023)
Shui Yu, Bruce Gu, Youyang Qu, Xiaodong Wang
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R1,776
Discovery Miles 17 760
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Ships in 10 - 15 working days
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This book constitutes the refereed post-conference proceedings of
the 17th EAI International Conference on Tools for Design,
Implementation and Verification of Emerging Information
Technologies, TridentCom 2022, which was held in Melbourne,
Australia, in November 23-25,2022. The 11 full papers were selected
from 30 submissions and deal the emerging technologies of big data,
cyber-physical systems and computer communications. The papers are
grouped in thematical sessions on network security; network
communication; network services; mobile and ad hoc networks;
blockchain; machine learning.
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