Location-based services (LBSs) typically crowdsource geo-tagged data from mobile users. Collecting more data will generally improve the utility for LBS providers; however, it also leads to more privacy exposure of users’ mobility patterns. Although the tension between data utility and user privacy has been recognized, there lacks a solution that determines how much data to collect—in both spatial and temporal domains—is the “best” for both mobile users and the service provider. This article proposes a strategy toward making an optimal tradeoff such that a user submits data only if her mobility privacy will not be compromised and the data utility of the LBS provider will be sufficiently improved. To this end, we first define and formulate a concept called privacy exposure , which incorporates both the spatial distribution and the temporal transition of a user’s activity points . Second, we define and quantify data utility in terms of spatial repetitions and temporal closeness among data based on an economic principle. Then, we propose a PRivacy-preserving and UTility-Enhancing Crowdsourcing (PRUTEC) algorithm to determine, on behalf of each mobile user, whether a newly sensed piece of data should be submitted to the LBS provider. Our simulation demonstrates that PRUTEC improves the data utility of the service provider with a much less amount of data to collect and reduces privacy exposure for mobile users while collecting useful data continuously.

中文翻译：

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人群隐私

基于位置的服务 (LBS) 通常从移动用户众包地理标记数据。收集更多数据通常会提高 LBS 提供商的效用；然而，它也导致用户移动模式的更多隐私暴露。尽管数据实用性和用户隐私之间的紧张关系已得到认可，但仍缺乏一种解决方案来确定在空间和时间域中收集多少数据对于移动用户和服务提供商来说都是“最佳”的。本文提出了一种进行最佳权衡的策略，这样用户只有在她的移动隐私不会受到损害并且 LBS 提供者的数据效用得到充分改善的情况下才提交数据。为此，我们首先定义并制定了一个概念，称为隐私暴露，它结合了用户的空间分布和时间转换活动点. 二、我们定义和量化数据实用程序基于经济原理的数据之间的空间重复和时间接近性。然后，我们提出了一种保护隐私和增强实用性的众包 (PRUTEC) 算法，以代表每个移动用户确定是否应将新检测到的数据提交给 LBS 提供商。我们的模拟表明，PRUTEC 提高了服务提供商的数据效用，需要收集的数据量要少得多，并在持续收集有用数据的同时减少移动用户的隐私暴露。