In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak.

中文翻译：

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具有不受信任服务器的空间众包中基于隐私的批量任务分配

在本文中，我们研究了空间众包中的隐私保护任务分配，其中工作人员和任务在发布到服务器之前的位置都受到地理不可区分性（基于位置的系统的差分隐私概念）的干扰. 与之前研究的在线设置不同，每个任务在到达时立即分配，我们针对基于批处理的设置，其中服务器在一批任务到达后最大化成功分配的任务数量。为了实现这一目标，我们提出了 k-Switch 解决方案，它首先根据工作人员/任务之间的扰动距离将工作人员分成小组，然后利用基于同态加密 (HE) 的安全计算来增强任务分配。此外，我们通过限制 k 下小组的大小来加快基于 HE 的计算。大量实验表明，就成功分配任务的数量而言，k-Switch 解决方案将基于批处理的基线提高了 5.9 倍，将现有在线解决方案提高了 1.74 倍，并且没有隐私泄漏。