Towards secure and truthful task assignment in spatial crowdsourcing

Zhai, Dongjun; Sun, Yu-E; Liu, An; Li, Zhixu; Liu, Guanfeng; Zhao, Lei; Zheng, Kai

doi:10.1007/s11280-018-0638-2

Cited by 31 publications

(8 citation statements)

References 35 publications

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“…Other crowdsourcing setting. There are other related works in the spatial crowdsourcing spectrum [24,25,28]. Different from the task assignment problem, data publishing has been considered in [25].…”

Section: Related Workmentioning

confidence: 99%

“…Different from the task assignment problem, data publishing has been considered in [25]. The truthful rather than privacy-preserving task assignment is considered in [28]. Privacy-preserving crowd-sensing is considered in [24], and the focus is to protect the locations of workers when they report their sensing results, rather than considering our task assignment setting, where workers need to move to a specified location of the assigned task, and both locations (tasks and workers) are perturbed with differential privacy.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

Wang

Zheng

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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In this paper, we study the privacy-preserving task assignment problem in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for locationbased systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the 𝑘-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under 𝑘. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the 𝑘-Switch solution improves batch-based baselines by 5.9× and the existing online solution by 1.74×, with no privacy leak. CCS CONCEPTS• Security and privacy → Domain-specific security and privacy architectures; • Information systems → Location based services; Crowdsourcing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

Wang

Zheng

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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“…In the reverse auction-based incentive model, DPs act as bidders who submit bids to compete for a task, and SP acts as the auctioneer, who aims to maximize its own benefit. Inspired by the work in [41], a number of incentive work based on reverse auction has to be carried out, but only a few of them [34,48,69,74] take into account the privacy issues. In this subsection, we focus on analyzing the requirement fulfillment of these schemes.…”

Section: B) Privacy Protection In Credit-based Incentive (Cr)mentioning

confidence: 99%

“…Yang et al adopted k-anonymity for bid privacy preservation and proposed three auctionbased incentive location privacy protection mechanisms [74]. The first solution is applicable to the scenario where all DCs have the same privacy requirements; the second one is proposed to deal with different privacy requirements in the real world; the last one is suitable for the case where a DP can cheat on both their valuations and degree requirement.…”

Section: B) Privacy Protection In Credit-based Incentive (Cr)mentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

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“…Moreover in a previous study, Liu et al [20] adopted Paillier Cryptosystem and KD-tree to build a secure index based on the dual-server setting to address the privacy-preserving and efficiency issues. Unfortunately, the huge update overhead makes this scheme unsuitable for the practical SC services since worker locations are dynamically changing rather than being static [21].…”

Section: Introductionmentioning

confidence: 99%

A Secure and Efficient Task Matching Scheme for Spatial Crowdsourcing

Zhou

Lin

et al. 2020

IEEE Access

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The sharing economy has greatly promoted the rapid development and application of spatial crowdsourcing. Although privacy-preserving spatial task matching as an indispensable part has been extensively explored, existing schemes cannot be deployed into the practical environment due to drawbacks in the one-side location protection, the matching efficiency, and the dynamic updates. In this study, we propose a novel Secure and Efficient Spatial Task Matching framework (SESTM) with utilizing multiuser searchable encryption and secure index technique, which enables to preserve the location privacy of requesters and workers while achieving efficient task allocation and good user scalability. Specifically, requesters firstly transform and encrypt their task locations before being outsourced, and we secondly design a secure and dynamic tree-based index SD-Tree for SC-server to merge these uploaded encrypted data without knowing their underlying content. Finally, SESTM provides efficient task matching services for multiple workers based on encrypted queries. Furthermore, SD-Tree also provides fast delete and insert operations under logarithmic time to reduce the dynamic update overhead for real SC services. Extensive theoretical analysis and performance evaluation demonstrate the practicality of our method.

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Towards secure and truthful task assignment in spatial crowdsourcing

Cited by 31 publications

References 35 publications

Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

Privacy protection in mobile crowd sensing: a survey

A Secure and Efficient Task Matching Scheme for Spatial Crowdsourcing

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