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    Efficient task assignment in spatial crowdsourcing with worker and task privacy protection

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    GeoInformatica2017a.pdf
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    2.404Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Liu, An
    Wang, Weiqi
    Shang, Shuo
    Li, Qing
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2017-08-01
    Online Publication Date
    2017-08-01
    Print Publication Date
    2018-04
    Permanent link to this record
    http://hdl.handle.net/10754/625718
    
    Metadata
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    Abstract
    Spatial crowdsourcing (SC) outsources tasks to a set of workers who are required to physically move to specified locations and accomplish tasks. Recently, it is emerging as a promising tool for emergency management, as it enables efficient and cost-effective collection of critical information in emergency such as earthquakes, when search and rescue survivors in potential ares are required. However in current SC systems, task locations and worker locations are all exposed in public without any privacy protection. SC systems if attacked thus have penitential risk of privacy leakage. In this paper, we propose a protocol for protecting the privacy for both workers and task requesters while maintaining the functionality of SC systems. The proposed protocol is built on partially homomorphic encryption schemes, and can efficiently realize complex operations required during task assignment over encrypted data through a well-designed computation strategy. We prove that the proposed protocol is privacy-preserving against semi-honest adversaries. Simulation on two real-world datasets shows that the proposed protocol is more effective than existing solutions and can achieve mutual privacy-preserving with acceptable computation and communication cost.
    Citation
    Liu A, Wang W, Shang S, Li Q, Zhang X (2017) Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica. Available: http://dx.doi.org/10.1007/s10707-017-0305-2.
    Sponsors
    Research reported in this publication was partially supported by KAUST and Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61632016, 61402313, 61472337), and has been benefited from discussions with Dr. Ke Sun in MINE lab at KAUST.
    Publisher
    Springer Nature
    Journal
    GeoInformatica
    DOI
    10.1007/s10707-017-0305-2
    Additional Links
    https://link.springer.com/article/10.1007%2Fs10707-017-0305-2
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10707-017-0305-2
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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