Anonymity-Based Privacy-Preserving Task Assignment in Spatial Crowdsourcing

Abstract

The ubiquity of mobile device and wireless networks flourishes the market of Spatial Crowdsourcing (SC), 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 SC-server usually needs to collect location information of all workers. During this process, there is a significant security concern, that is, SC-server may not be trustworthy, so it brings about a threat to workers location privacy. In this paper, we focus on the privacy-preserving task assignment in SC. By introducing a semi-honest third party, we present an approach for task assignment in which location privacy of workers can be protected in a k-anonymity manner. We theoretically show that the proposed model is secure against semi-honest adversaries. Experimental results show that our approach is efficient and can scale to real SC applications.