PriRadar: A Privacy-Preserving Framework for Spatial Crowdsourcing

Abstract

Privacy leakage is a serious issue in spatial crowdsourcing in various scenarios. In this paper, we study privacy protection in spatial crowdsourcing. The main challenge is to efficiently assign tasks to nearby workers without needing to know the exact locations of tasks and workers. To address this problem, we propose a privacy-preserving framework without online trusted third parties. We devise a grid-based location protection method, which can protect the locations of workers and tasks while keeping the distance-aware information on the protected locations such that we can quantify the distance between tasks and workers. We propose an efficient task assignment algorithm, which can instantly assign tasks to nearby workers on encrypted data. To protect the task content, we leverage both attribute-based encryption and symmetric-key encryption to establish secure channels through servers, which ensures that the task is delivered securely and accurately by any untrusted server. Moreover, we analyze the security properties of our method. We have conducted real experiments on real-world datasets. Experimental results show that our method outperforms existing approaches.