With the ubiquity of mobile devices and wireless networks, Spatial Crowdsourcing (SC) has earned considerable importance and attention as a new strategy of problem-solving. Tasks in SC have location constraints and workers need to move to certain locations to perform them. Current studies mainly focus on maximizing the benefits of the SC platform. However, user average waiting time, which is an important indicator of user experience, has been overlooked. To enhance user experience, the SC platform needs to collect lots of data from both workers and users. During this process, the private information may be compromised if the platform is not trustworthy. In this paper, we first define user experience-driven secure task assignment problem and propose two privacy-preserving online task assignment strategies to minimize the average waiting time. We securely construct an encrypted bipartite graph to protect private data. Based on this encrypted graph, we propose a secure Kuhn-Munkres algorithm to realize task assignment without privacy disclosure. Theoretical analysis shows the security of our approach and experimental results demonstrates its efficiency and effectiveness.

中文翻译：

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用户体验驱动的空间众包中的安全任务分配

随着移动设备和无线网络的普及，空间众包（SC）作为解决问题的新策略已赢得了相当大的重视和关注。SC中的任务具有位置限制，工作人员需要移动到某些位置才能执行它们。当前的研究主要集中在最大化SC平台的利益上。但是，作为用户体验重要指标的用户平均等待时间已被忽略。为了增强用户体验，SC平台需要从工作人员和用户那里收集大量数据。在此过程中，如果平台不可信，则可能会破坏私人信息。在本文中，我们首先定义了用户体验驱动的安全任务分配问题，并提出了两种保护隐私的在线任务分配策略，以最大程度地减少平均等待时间。我们安全地构造了一个加密的二分图以保护私有数据。基于该加密图，我们提出了一种安全的Kuhn-Munkres算法，可以在不泄露隐私的情况下实现任务分配。理论分析表明了该方法的安全性，实验结果表明了该方法的有效性。