TY - JOUR
T1 - Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles
AU - Li, Zihan
AU - Zhao, Mingyang
AU - Chen, Guanyu
AU - Zhang, Chuan
AU - Wu, Tong
AU - Zhu, Liehuang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Internet of vehicles
KW - privacy preservation
KW - task allocation
UR - http://www.scopus.com/inward/record.url?scp=85147134256&partnerID=8YFLogxK
U2 - 10.1109/OJCS.2022.3222363
DO - 10.1109/OJCS.2022.3222363
M3 - Article
AN - SCOPUS:85147134256
SN - 2644-1268
VL - 3
SP - 295
EP - 303
JO - IEEE Open Journal of the Computer Society
JF - IEEE Open Journal of the Computer Society
ER -