Towards secure and truthful task assignment in spatial crowdsourcing

Dongjun Zhai, Yue Sun, An Liu*, Zhixu Li, Guanfeng Liu, Lei Zhao, Kai Zheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


The ubiquity of mobile device and wireless networks flourishes the market of spatial crowdsourcing, in which location constrained tasks are sent to workers and expected to be performed in some designated locations. To obtain a global optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this process, there is a significant security concern, that is, the platform may not be trustworthy, so it brings about a threat to workers location privacy. In this paper, to tackle the privacy-preserving task assignment problem, we propose a privacy-preserving reverse auction based assignment model which consists of two key parts. In the first part, we generalize private location to travel cost and protect it by an anonymity based data aggregation protocol. In the second part, we propose a reverse auction task assignment algorithm, which is a truthful incentive mechanism, to encourage workers to offer authentic data. We theoretically show that the proposed model is secure against semi-honest adversaries. Experimental results show that our model is efficient and can scale to real SC applications.

Original languageEnglish
Pages (from-to)2017-2040
Number of pages24
JournalWorld Wide Web
Issue number5
Publication statusPublished - 15 Sep 2019
Event18th International Conference on Web Information Systems Engineering (WISE) - Moscow
Duration: 7 Oct 201711 Oct 2017


  • Privacy-preserving
  • Reverse auction
  • Spatial crowdsourcing
  • Task assignment


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