Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing

Liu, Hong; Zhang, Yan; Yang, Tao

doi:10.1109/mnet.2018.1700344

Cited by 342 publications

(122 citation statements)

References 13 publications

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“…Numerical results have indicated that the proposed incentive scheme can attract more data owners with high-quality local training data to ensure efficient federated learning and also optimize the utilities of both the task publishers and the data owners. For further work, we will consider using blockchains to ensure reliability of local model updates when formulating the incentive mechanism for reliable federated learning in mobile networks [17], [18].…”

Section: Numerical Resultsmentioning

confidence: 99%

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

Kang

Xiong

Niyato

et al. 2019

2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

170

101

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To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to join model training have been largely overlooked. The mobile devices suffer from considerable overhead in terms of computation and communication during the federated model training process. Without well-designed incentive, self-interested mobile devices will be unwilling to join federated learning tasks, which hinders the adoption of federated learning. To bridge this gap, in this paper, we adopt the contract theory to design an effective incentive mechanism for simulating the mobile devices with high-quality (i.e., high-accuracy) data to participate in federated learning. Numerical results demonstrate that the proposed mechanism is efficient for federated learning with improved learning accuracy.

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Section: Numerical Resultsmentioning

confidence: 99%

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

Kang

Xiong

Niyato

et al. 2019

2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

170

101

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“…Khan et al extensively studied a set of 13 reference Fog computing applications and analyzed which of 12 potential security issues apply to which application . One may expect that some Fog computing applications—such as driving assistance applications for communicating vehicles—will be shared by a large number of entities, which hints at Blockchain‐based solutions due to the distributed nature of Blockchain. As Blockchain provides tamper‐proof storage and uses consensus mechanism to confirm past events, it can be used as a security means.…”

Section: State Of the Art/related Workmentioning

confidence: 99%

A systematic approach toward security in Fog computing: Assets, vulnerabilities, possible countermeasures

et al. 2020

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Fog computing is an emerging paradigm in the Internet of Things (IoT) space, consisting of a middle computation layer, sitting between IoT devices and Cloud servers. Fog computing provides additional computing, storage, and networking resources in close proximity to where data is being generated and/or consumed. As the Fog layer has direct access to data streams generated by IoT devices and responses/commands sent from the Cloud, it is in a critical position in terms of security of the entire IoT system. Currently, there is no specific tool or methodology for analysing the security of Fog computing systems in a comprehensive way.Generic security evaluation procedures applicable to most information technology products are time consuming, costly, and badly suited to the Fog context. In this article, we introduce a methodology for evaluating the security of Fog computing systems in a systematic way. We also apply our methodology to a generic Fog computing system, showcasing how it can be purposefully used by security analysts and system designers. K E Y W O R D Sattack, common criteria, fog computing, methodology, security INTRODUCTIONFog computing is an emerging technology which enriches Cloud computing with additional compute, storage and networking resources in close proximity with the end-user devices which generate and/or consume data streams. 1 With the development of Internet of Things (IoT) systems and applications, increasing volumes of data are being produced by IoT devices at the edges of the network. In this situation, it is often not feasible to send all IoT data to a remote Cloud data center and expect acceptable Quality of Service, especially for applications with low-latency requirements such as augmented reality, industrial control systems, and video streaming. Moreover, many applications such as quantified self which use wearable sensors to monitor individuals life often deal with sensitive personal data. In solely Cloud-based approaches for these applications, all these sensitive data would be sent to the Cloud for processing, leaving the user with little control over the usage of their data.An IoT system architecture with support of Fog computing comprises at least the following three layers: 2 (a) An IoT device layer (comprising the actual sensors and actuators); (b) A Fog computing layer located very close to the IoT devices; and (c) A Cloud computing layer.Fog computing therefore acts as a middle layer, sitting between the Cloud and the IoT devices. Depending on the application, some parts of the computation may be delegated to the Fog layer, which prevents one from having to send Softw: Pract Exper. 2020;50:973-997. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 973 974 FARHADI et al.raw data to the Cloud. As Fog computing servers process local data locally, we also expect a lower usage of long-distance network bandwidth as well as reduced response times. Also, the user data are now processed by computing elements which are geographically close to where data is generated, l...

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“…LSB uses a lightweight hash function and lightweight consensus algorithm in order to achieve scalability, security, and privacy. Blockchain is also used to cater security in electric vehicles, cloud and edge computing [103] which use lightweight cryptographic primitives like lightweight symmetric key encryption.…”

Section: Lightweight Cryptographymentioning

confidence: 99%

SoK of Used Cryptography in Blockchain

2019

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The underlying fundaments of blockchain are cryptography and cryptographic concepts that provide reliable and secure decentralized solutions. Although many recent papers study the use-cases of blockchain in different industrial areas, such as finance, health care, legal relations, IoT, information security, and consensus building systems, only few studies scrutinize the cryptographic concepts used in blockchain. To the best of our knowledge, there is no Systematization of Knowledge (SoK) that gives a complete picture of the existing cryptographic concepts which have been deployed or have the potential to be deployed in blockchain. In this paper, we thoroughly review and systematize all cryptographic concepts which are already used in blockchain. Additionally, we give a list of cryptographic concepts which have not yet been applied but have big potentials to improve the current blockchain solutions. We also include possible instantiations of these cryptographic concepts in the blockchain domain. Last but not least, we explicitly postulate 21 challenging problems that cryptographers interested in blockchain can work on.

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Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing

Cited by 342 publications

References 13 publications

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

A systematic approach toward security in Fog computing: Assets, vulnerabilities, possible countermeasures

SoK of Used Cryptography in Blockchain

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