iTAM: Bilateral Privacy-Preserving Task Assignment for Mobile Crowdsensing

Zhao, Bowen; Tang, Shaohua; Liu, Ximeng; Zhang, Xinglin; Chen, Wei-Neng

doi:10.1109/tmc.2020.2999923

Cited by 50 publications

(16 citation statements)

References 41 publications

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“…Task Allocation Methods considering Privacy. In the process of task assignment and data collection, the MCS system collects detailed sensing data from users, which undoubtedly poses a threat to the privacy of users [69][70][71][72][73].…”

Section: Task Allocation Methods Considering Costs Andmentioning

confidence: 99%

“…Each participant in the sensing task uploads their personal privacy level and obfuscated distance to the server rather than their true distance or location. In literature [72], Zhao et al proposed a bilateral privacy-preserving task allocation method that cannot only protect the privacy of task participants but also protect the privacy of task requesters and minimize travel distance. In literature [73], Wang et al proposed a task allocation algorithm that performs task allocation based on the mapping accuracy of sensing tasks and participants while preserving participants' location privacy.…”

Section: Task Allocation Methods Considering Costs Andmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Application and Key Techniques for Mobile Crowdsensing

Wang

2022

Wireless Communications and Mobile Computing

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Mobile crowdsensing (MCS) uses ubiquitous smart devices and network access technology to migrate sensing tasks from a centralized platform to a distributed computing terminal across time and space and provides new approaches for solving large-scale, diversified, and complex sensing problems. MCS research is of great importance and has attracted wide attention from researchers. Accordingly, this article summarizes the current research status of MCS. First, the typical applications of MCS are summarized. Then, the research status of key technologies, such as task allocation, incentivizing, data transmission, quality assurance, and evaluation methods, is considered. Finally, future research directions of MCS are given to provide a reference for in-depth MCS research.

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Section: Task Allocation Methods Considering Costs Andmentioning

confidence: 99%

Section: Task Allocation Methods Considering Costs Andmentioning

confidence: 99%

A Survey of Application and Key Techniques for Mobile Crowdsensing

Wang

2022

Wireless Communications and Mobile Computing

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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%

“…For example, Yang et al [26] utilized the bilinear map to design a privacy-preserving task allocation scheme. Based on the Paillier cryptosystem, Zhao et al [28] proposed a bilateral privacy-preserving task allocation scheme. In these schemes, user privacy is preserved well under the chosen-plain attack.…”

Section: Introductionmentioning

confidence: 99%

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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“…Prior studies [10], [11] require requester's requirements and workers' interests to achieve accurate and efficient task assignment. In spatial crowdsensing like [12], [13], task assignment is usually based on the distance between the target and workers. These schemes did not take workers' capabilities into account, specifically whether he has the equipment or special skills to complete the task.…”

mentioning

confidence: 99%

ATM: Attribute-Based Privacy-Preserving Task Assignment and Incentive Mechanism for Crowdsensing

Yang

Xian

2021

IEEE Access

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Crowdsensing is a practical component in Internet-of-Things, in which task requesters outsource sensing tasks to workers by a crowdsensing server (CS). Task assignment and incentive design are two essential parts of crowdsensing. However, due to the semi-honest CS and malicious workers, there exist several security and fairness issues. First, during task assigning and reward distributing, CS can get the private information of both task requester and workers such as identity, attributes. Second, malicious workers who are unable to execute tasks can get the task content, which leaks task privacy. Third, most auction-based incentive mechanism only considers the biddings of workers and ignores the real capabilities of workers. To this end, we propose a privacy-preserving fine-grained task assignment scheme for the crowdsensing system. Moreover, we also design a novel incentive mechanism based on the capabilities of workers. To be specific, a ciphertext-policy attribute-based encryption (CP-ABE) scheme with the characteristics of hidden policy is adopted to select workers and protect the privacy that of both requirements and workers' attributes. We quantify the capabilities of workers based on the workers' attributes and further determine the workers' reward according to their capabilities. Detailed security analysis demonstrates that our proposed scheme can protect workers' identity privacy, attribute privacy, and data confidentiality. Besides, the extensive experiments show that our incentive mechanism can effectively motivate workers.

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iTAM: Bilateral Privacy-Preserving Task Assignment for Mobile Crowdsensing

Cited by 50 publications

References 41 publications

A Survey of Application and Key Techniques for Mobile Crowdsensing

A Survey of Application and Key Techniques for Mobile Crowdsensing

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

ATM: Attribute-Based Privacy-Preserving Task Assignment and Incentive Mechanism for Crowdsensing

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