Protecting Location Privacy in Spatial Crowdsourcing

Recently, spatial crowdsourcing has attracted wide attention in both the research community and industry, one of which is the eMarket platform. It enables requesters to release spatial tasks (i.e., tasks related to a location) and expect them to be performed by workers (i.e., users with smart mobile devices). One of the key functions of such platform is spatial tasks assignment. The traditional solutions to the tasks assignment problem require workers to disclose their locations to the spatial crowdsourcing server (SC-server), which are untrustworthy entities. In this paper, we employ the peer-to-peer spatial K-anonymity to protect the workers' location privacy. However, it will result in the consequence that various spatial tasks can't be performed. To improve the spatial task assignment, we propose an optimized scheme for spatial task assignment without compromising the workers' location privacy, and verify the effect through our experiments.