TASC: Efficient Task Assignment in Spatial Crowdsourcing with Workers Privacy Protection

Crowdsourcing has open not just opportunities but also options for people to work together without boundaries it is the future of collaboration. Task assignments are the main function of any crowdsourcing platform. Many recent studies focused on the workers privacy without taking into consideration the overall task matching utility score. In this paper, we adapt the batch matching method to maximizing the overall task assignment in spatial crowdsourcing while maintaining the crowd workers privacy using the DCentroid scheme. Then, theoretically evaluates the performance of the proposed task assignment scheme to show the effectiveness of this method. This paper introduces (TASC) a task assignment approach with worker location privacy protection in Spatial Crowdsourcing (SC). The research theoretically analyzes TASC framework and guarantees the crowd workers privacy while minimizing the task waiting time, worker travel distance, and system overhead.