Abstract
Spatial Crowdsourcing (SC) is a new valuable paradigm, relies on crowd workers to perform a set of spatial-temporal tasks at specific locations. It has garnered attention in collecting and processing social, environmental, and other spatio-temporal data by the contribution of individuals, communities and groups of workers in the physical world. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, which requires the workers to be physically traveling to the tasks' locations in order to perform them, i.e., taking photos or collecting real time weather information at pre-specified location. Existing solutions require crowd workers to disclose their precise locations to untrustworthy service providers. Location updates and tracking in spatial crowdsourcing raise several privacy concerns in that malicious parties could snoop on crowd workers' whereabouts. Thus, the crowd workers' privacy could be compromised by disclosing their locations to untrusted and possibly malicious parties. This paper provides a novel framework called Dummies' Centroid (DCentroid), which aims at preserving location privacy for crowd workers in SC. The framework adapts an anonymous communication technique using a dummy based approach to generate dummy locations, i.e. decoy locations, and send their centroid points (pseudolocations) to service providers for processing. This paper theoretically analyzes the DCentroid framework and guarantees the crowd workers' privacy, while preserving the functionality of SC, such as the success rate of task assignments, worker travel distances, and system overhead. Practical experimentation on real-world datasets shows that the DCentroid framework protects the crowd workers' location privacy without affecting the various performance parameters of task assignment.