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Toward location privacy protection in Spatial crowdsourcing

Author

Listed:
  • Hang Ye
  • Kai Han
  • Chaoting Xu
  • Jingxin Xu
  • Fei Gui

Abstract

Spatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, which unavoidably attracts attention to the privacy-preserving of the workers’ locations. In this article, we propose a novel framework that can protect the location privacy of the workers and the requesters when assigning tasks to workers. Our scheme is based on mathematical transformation to the location while providing privacy protection to workers and requesters. Moreover, to further preserve the relative location between workers, we generate a certain amount of noise to interfere the spatial crowdsourcing server. Experimental results on real-world data sets show the effectiveness and efficiency of our proposed framework.

Suggested Citation

  • Hang Ye & Kai Han & Chaoting Xu & Jingxin Xu & Fei Gui, 2019. "Toward location privacy protection in Spatial crowdsourcing," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:3:p:1550147719830568
    DOI: 10.1177/1550147719830568
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
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    Cited by:

    1. Farid Yessoufou & Salma Sassi & Elie Chicha & Richard Chbeir & Jules Degila, 2024. "User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks," Future Internet, MDPI, vol. 16(9), pages 1-29, August.

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