Abstract
Spatial crowdsourcing is a form of location-based crowdsourcing. With the spread use of mobile phones and smart devices that can share location, spatial crowdsourcing gained a lot of attention, especially in ride-hailing services. This paper evaluates the performance of a proposed spatial crowdsourcing task assignment approach to increase the task assignment rate while preserving the location privacy of the crowd workers. The overall experiments on real-world data sets show that the proposed approach results in the maximal total number of assigned tasks without significant disclosure of crowd workers' locations.