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Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles | IEEE Journals & Magazine | IEEE Xplore

Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles


Abstract:

Nowadays, task allocation has attracted increasing attention in the Internet of Vehicles. To efficiently allocate tasks to suitable workers, users usually need to publish...Show More

Abstract:

Nowadays, task allocation has attracted increasing attention in the Internet of Vehicles. To efficiently allocate tasks to suitable workers, users usually need to publish their task interests to the service provider, which brings a serious threat to users' privacy. Existing task allocation schemes either cannot comprehensively preserve user privacy (i.e., requester privacy and worker privacy) or introduce tremendous resource overhead. In this paper, we propose an efficient and privacy-preserving versatile task allocation scheme (PPVTA) for the Internet of vehicles. Specifically, we utilize the randomizable matrix multiplication technique to preserve requester privacy and worker privacy. Then, the polynomial fitting technique is leveraged to enrich the randomizable matrix multiplication to support versatile task allocation functions, such as threshold-based task allocation (PPVTA-I), conjunctive task allocation (PPVTA-II), and task allocation with bilateral access control (PPVTA-III). We formally analyze the security of our constructions to prove the security under the chosen-plain attack. Based on a prototype, experimental results demonstrate that our constructions have acceptable efficiency in practice.
Published in: IEEE Open Journal of the Computer Society ( Volume: 3)
Page(s): 295 - 303
Date of Publication: 29 November 2022
Electronic ISSN: 2644-1268

Funding Agency:


References

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