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

Zihan Li, Mingyang Zhao, Guanyu Chen, Chuan Zhang*, Tong Wu, Liehuang Zhu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)295-303
Number of pages9
JournalIEEE Open Journal of the Computer Society
Volume3
DOIs
Publication statusPublished - 2022

Keywords

  • Internet of vehicles
  • privacy preservation
  • task allocation

Fingerprint

Dive into the research topics of 'Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles'. Together they form a unique fingerprint.

Cite this