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Attribute-Based Encryption With Parallel Outsourced Decryption for Edge Intelligent IoV

delete2020-11-01
delete190
PRE
AI
C
Chaosheng Feng
K
Keping Yu *
M
Moayad Aloqaily
M
Mamoun Alazab
吕
吕智涵 (Zhihan Lv)
S
Shahid Mumtaz
DOI:10.1109/TVT.2020.3027568delete
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摘要

摘要

En 中文
Edge intelligence is an emerging concept referring to processes in which data are collected and analyzed and insights are delivered close to where the data are captured in a network using a selection of advanced intelligent technologies. As a promising solution to solve the problems of insufficient computing capacity and transmission latency, the edge intelligence-empowered Internet of Vehicles (IoV) is being widely investigated in both academia and industry. However, data sharing security in edge intelligent IoV is a challenge that should be solved with priority. Although attribute-based encryption (ABE) is capable of addressing this challenge, many time-consuming modular exponential operations and bilinear pair operations as well as serial computing cause ABE to have a slow decryption speed. Consequently, it cannot address the response time requirement of edge intelligent IoV. Given this problem, an ABE model with parallel outsourced decryption for edge intelligent IoV, called ABEM-POD, is proposed. It includes a generic parallel outsourced decryption method for ABE based on Spark and MapReduce. This method is applicable to all ABE schemes with a tree access structure and can be applied to edge intelligent IoV. Any ABE scheme based on the proposed model not only supports parallel outsourced decryption but also has the same security as the original scheme. In this paper, ABEM-POD has been applied to three representative ABE schemes, and the experiments show that the proposed ABEM-POD is efficient and easy to use. This approach can significantly improve the speed of outsourced decryption to address the response time requirement for edge intelligent IoV.
Keyword:
Cloud computing
Encryption
Outsourcing
Computational modeling
Electronic mail
Attribute-based Encryption
decryption outsourcing
edge intelligence
IoV
parallel computing
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期刊

IEEE Transactions on Vehicular Technology 封面图
IEEE Transactions on Vehicular Technology
IF:
7.1
论文数:
1.8W
被引数:
6.6W

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Waseda University
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Qingdao University
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Charles Darwin University
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Sichuan Normal University
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