A Novel Location Privacy-Preserving Task Allocation Scheme for Spatial Crowdsourcing

Abstract

With the increasing popularity of big data and sharing economics, spatial crowdsourcing as a new computing paradigm has attracted the attention of both academia and industry. Task allocation is one of the indispensable processes in spatial crowdsourcing, but how to allocate tasks efficiently while protecting location privacy of tasks and workers is a tough problem. Most of the existing works focus on the selection of the workers privately. Few of them present solutions for secure problems in task delivery. To address this problem, we propose a novel privacy protection scheme that not only protects the location privacy of workers and tasks but also enables secure delivery of tasks with very little overhead. We first use the paillier homomorphic cryptosystem to protect the privacy of workers and tasks, then calculate travel information securely. Finally, let workers restore the tasks’ location. In our scheme, only workers who meet the requirements can get the exact location of tasks. In addition, we prove the security of our method under the semi-honest model. Extensive experiments on real-world data sets demonstrate that our scheme achieves practical performance in terms of computational overhead and travel cost.