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Privacy-Enhancing Fog Computing and Its Applications

  • Xiaodong Lin
  • Jianbing Ni
  • Xuemin (Sherman) Shen

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
    Pages 1-8
  3. Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
    Pages 9-33
  4. Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
    Pages 35-53
  5. Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
    Pages 55-66
  6. Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
    Pages 67-85
  7. Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
    Pages 87-89

About this book

Introduction

This SpringerBrief  covers the security and privacy challenges in fog computing, and proposes a  new secure and privacy-preserving mechanisms to resolve these challenges for securing fog-assisted IoT applications. Chapter 1 introduces the architecture of fog-assisted IoT applications and the security and privacy challenges in fog computing. Chapter 2 reviews several promising privacy-enhancing techniques and illustrates examples on how to leverage these techniques to enhance the privacy of users in fog computing. Specifically,  the authors divide the existing privacy-enhancing techniques into three categories: identity-hidden techniques, location privacy protection and data privacy enhancing techniques. The research is of great importance since security and privacy problems faced by fog computing impede the healthy development of its enabled IoT applications.

 

With the advanced privacy-enhancing techniques, the authors propose three secure and privacy-preserving protocols for fog computing applications, including smart parking navigation, mobile crowdsensing and smart grid.  Chapter 3 introduces identity privacy leakage in smart parking navigation systems, and proposes a privacy-preserving smart parking navigation system to prevent identity privacy exposure and support efficient parking guidance retrieval through road-side units (fogs) with high retrieving probability and security guarantees. Chapter 4 presents the location privacy leakage, during task allocation in mobile crowdsensing, and propose a strong privacy-preserving task allocation scheme that enables location-based task allocation and reputation-based report selection without exposing knowledge about the location and reputation for participators in mobile crowdsensing. Chapter 5 introduces the data privacy leakage in smart grid, and proposes an efficient and privacy-preserving smart metering protocol to allow collectors (fogs) to achieve real-time measurement collection with privacy-enhanced data aggregation. Finally, conclusions and future research directions are given in Chapter 6.

 This brief validates the significant feature extension and efficiency improvement of IoT devices without sacrificing the security and privacy of users against dishonest fog nodes. It also provides valuable insights on the security and privacy protection for fog-enabled IoT applications. Researchers and professionals who carry out research on security and privacy in wireless communication will want to purchase this SpringerBrief.  Also, advanced level students,  whose main research area is mobile network security will also be interested in this SpringerBrief. 


Keywords

Fog computing privacy conversion cyber security Internet of Things (IoT) Mobile Crowdsensing security privacy vehicular ad hoc network smart grid mobile sensing identity privacy location privacy data privacy privacy-enhanced technology smart parking smart-metering

Authors and affiliations

  • Xiaodong Lin
    • 1
  • Jianbing Ni
    • 2
  • Xuemin (Sherman) Shen
    • 3
  1. 1.Department of Physics and Computer ScienceWilfrid Laurier UniversityWaterlooCanada
  2. 2.Electrical and Computer Engineering DepartmentUniversity of WaterlooWaterlooCanada
  3. 3.Electrical and Computer Engineering DepartmentUniversity of WaterlooWaterlooCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-02113-9
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-02112-2
  • Online ISBN 978-3-030-02113-9
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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