A Geo-Obfuscation Approach to Protect Worker Location Privacy in Spatial Crowdsourcing Systems

In Spatial Crowdsourcing (SC), workers are required to move to the task location to perform the tasks. To promote cost-effective crowdsourcing work, tasks need to be assigned to workers with low traveling costs. As such, workers are required to disclose their location information to SC servers, which may lead to serious privacy concerns. In this paper, we propose a geo-obfuscation approach to help workers protect their location information while ensuring the accuracy of traveling cost estimation in task assignment. We implemented our approach with a real-world experiment and the experimental results demonstrate the effectiveness of our approach in terms of both privacy and information accuracy.