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2018 | Buch

Privacy-Enhancing Fog Computing and Its Applications

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
A large number of physical “things”, embedded with sensors and actuators, exchange data with each other or the Internet through heterogeneous networks, which brings us to the era of Internet of Things (IoT) [1]. Currently, various devices, such as smart phones, appliances, traffic lights, wearable devices, vehicles and industrial sensors, are interconnected to offer a variety of services and applications in different domains, including smart city, e-healthcare, intelligent transportation and disaster response [2]. This innovation has brought new opportunities to improve our lives and promote the development of our society.
Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
Chapter 2. Privacy-Enhancing Technologies
Abstract
In this chapter, we will review several state-of-the-art privacy-enhancing techniques for identity, location and data privacy preservation.
Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
Chapter 3. Identity Privacy Protection in Smart Parking Navigation
Abstract
Due to the large volumes of modern vehicles in metropolises, finding a vacant parking space has become an irritating and frustrating problem for drivers, particularly in a congested area, such as sport centers, shopping malls, and downtown [1]. The extra traffic cruising for parking spaces brings serious social problems, including traffic congestion, vehicle accident, fuel waste, and air pollution [2]. Although Google Maps and portable navigators assist drivers to discover parking garages in destinations, drivers are then faced with a problem that no vacant parking space are available [3].
Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
Chapter 4. Location Privacy Protection in Mobile Crowdsensing
Abstract
With the increasingly popularity of user-centric mobile sensing and computing devices, e.g., smart phones, in-vehicle sensing devices and wearable devices, our knowledge of the physical world is extended by opening a new door to collect and process data about social events and natural phenomena [1, 2]. This alternative has triggered the emergence of mobile crowdsensing (MCS) services [3]. In MCS, individuals cooperatively sense data for the tasks released by customers and extract information to measure and map phenomena of common interests using their mobile devices [4].
Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
Chapter 5. Data Privacy Protection in Smart Grid
Abstract
Smart grid enhances the power grid with information and communication technologies, such as control systems, network communication, and computation facilities, to enable two-way exchange of electricity and information between operation centers and smart meters, while making the grid more reliable, efficient, secure and greener [1]. In smart grid, operation centers are allowed to collect and analyze real-time power consumption and local energy generation for distribution management, outage identification, state estimation and dynamic billing. The operation centers share electricity consumption to power plants, thereby help power plants to adjust energy production and reduce the demand to fire up costly and secondary power plans [2]. Not only could the customers access real-time usage data and electricity prices, but also decrease their energy consumption by shifting the uninterrupted activities from peak time to non-peak time.
Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
Chapter 6. Summary and Future Directions
Abstract
In this chapter, we summarize the monograph, and discuss several potential research topics for future work.
Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen
Metadaten
Titel
Privacy-Enhancing Fog Computing and Its Applications
verfasst von
Xiaodong Lin
Jianbing Ni
Xuemin (Sherman) Shen
Copyright-Jahr
2018
Electronic ISBN
978-3-030-02113-9
Print ISBN
978-3-030-02112-2
DOI
https://doi.org/10.1007/978-3-030-02113-9