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.

中文翻译：

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PriRadar：空间众包的隐私保护框架


隐私泄露是空间众包在各种场景下面临的严重问题。在本文中，我们研究空间众包中的隐私保护。主要挑战是有效地将任务分配给附近的工人，而不需要知道任务和工人的确切位置。为了解决这个问题，我们提出了一个没有在线可信第三方的隐私保护框架。我们设计了一种基于网格的位置保护方法，它可以保护工人和任务的位置，同时保留受保护位置的距离感知信息，以便我们可以量化任务和工人之间的距离。我们提出了一种高效的任务分配算法，可以根据加密数据立即将任务分配给附近的工作人员。为了保护任务内容，我们利用基于属性的加密和对称密钥加密在服务器之间建立安全通道，从而确保任何不受信任的服务器都能安全、准确地交付任务。此外，我们分析了我们方法的安全属性。我们对现实世界的数据集进行了真实的实验。实验结果表明我们的方法优于现有方法。