FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme

Zhang, Chuan; Zhao, Mingyang; Zhu, Liehuang; Zhang, Weiting; Wu, Tong; Ni, Jianbing

doi:10.1109/jsac.2022.3213341

Cited by 58 publications

(19 citation statements)

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“…Nowadays, to allocate tasks to suitable workers without compromising user privacy, some privacy-preserving task allocation schemes [18], [25]- [28], [32], [36], [38]- [41] have been proposed. Specifically, Gong et al [38] proposed the first task allocation framework with privacy preservation for crowdsourcing.…”

Section: A Related Workmentioning

confidence: 99%

“…Although these schemes can guarantee security under the chosen-plain attack, applying time-consuming tools also introduces tremendous resource overhead on each entity in the system, especially the service provider. To address these challenges above, inspired by matrix multiplication, some works are proposed to achieve efficient task allocation with security under the chosen-plain attack [18], [32], [36], [37]. Specifically, Ni et al [18] utilized proxy re-encryption to design an accurate task allocation scheme with privacy preservation.…”

Section: A Related Workmentioning

confidence: 99%

“…In this section, based on a prototype, we analyze the performance of our constructions, i.e., PPVTA-I, PPVTA-II, and PPVTA-III. We make comparisons between our constructions and the two most recent multi-keyword task allocation schemes FRUIT [36] and SETM [26].…”

Section: Performance Analysismentioning

confidence: 99%

“…In addition, these schemes usually cannot satisfy versatile task allocation functions, which significantly hinders their implementations. To address these challenges, several works have been proposed by combining the randomizable matrix multiplication technique [18], [36], [37]. For example, Zhang et al [36] utilized the randomized matrix multiplication technique to design a privacy-preserving task allocation scheme.…”

Section: Introductionmentioning

confidence: 99%

“…To address these challenges, several works have been proposed by combining the randomizable matrix multiplication technique [18], [36], [37]. For example, Zhang et al [36] utilized the randomized matrix multiplication technique to design a privacy-preserving task allocation scheme. In their scheme, tasks can be allocated to suitable workers without compromising user privacy.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Zhao

Chen

Zhang

et al. 2022

IEEE Open J. Comput. Soc.

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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.

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