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Differentially Private Online Task Assignment in Spatial Crowdsourcing: A Tree-based Approach

机译：空间众包中的差异私有在线任务分配：基于树的方法

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With spatial crowdsourcing applications such as Uber and Waze deeply penetrated into everyday life, there is a growing concern to protect user privacy in spatial crowdsourcing. Particularly, locations of workers and tasks should be properly processed via certain privacy mechanism before reporting to the untrusted spatial crowdsourcing server for task assignment. Privacy mechanisms typically permute the location information, which tends to make task assignment ineffective. Prior studies only provide guarantees on privacy protection without assuring the effectiveness of task assignment. In this paper, we investigate privacy protection for online task assignment with the objective of minimizing the total distance, an important task assignment formulation in spatial crowdsourcing. We design a novel privacy mechanism based on Hierarchically Well-Separated Trees (HSTs). We prove that the mechanism is ε-Geo-Indistinguishable and show that there is a task assignment algorithm with a competitive ratio of $Oleft( {rac{1}{{{arepsilon ^4}}}log N{{log }^2}k} ight)$, where is the privacy budget, N is the number of predefined points on the HST, and k is the matching size. Extensive experiments on synthetic and real datasets show that online task assignment under our privacy mechanism is notably more effective in terms of total distance than under prior differentially private mechanisms.
机译：随着诸如Uber和Waze之类的空间众包应用已深入到日常生活中，在空间众包中保护用户隐私越来越引起人们的关注。特别是，在报告给不受信任的空间众包服务器进行任务分配之前，应通过某种隐私机制适当地处理工人和任务的位置。隐私机制通常会置换位置信息，这往往会使任务分配无效。先前的研究仅提供有关隐私保护的保证，而不能确保任务分配的有效性。在本文中，我们研究了用于在线任务分配的隐私保护，目的是最小化总距离，这是空间众包中的重要任务分配公式。我们设计了一种基于层次分明的树（HST）的新颖的隐私机制。我们证明了该机制是ε-Geo不可区分的，并表明存在一种任务分配算法，其竞争比为$ O \ left（{\ frac {1} {{{\ varepsilon ^ 4}}} \ log N { {\ log} ^ 2} k} \ right）$，其中，是隐私预算，N是HST上预定义点的数量，k是匹配大小。在合成数据集和真实数据集上进行的大量实验表明，在总距离方面，我们的隐私机制下的在线任务分配比在先的差分私有机制下更有效。

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