ULPT: A User-Centric Location Privacy Trading Framework for Mobile Crowd Sensing

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages the power of ubiquitous mobile devices to acquire rich information regarding their surrounding environment. In many location-based sensing tasks, workers are required to associate their sensing reports with corresponding geographic coordinates. Such information leaves a trail of worker's historical location record which thus poses a severe threat to their location privacy. On the other hand, individual workers may perceive location privacy differently. Instead of following conventional solutions that aim to perfectly hide user privacy, this paper adopts a novel alternative approach. A user-centric location privacy trading framework, called ULPT, is constructed to facilitate location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform in an MCS task based on its own location privacy leakage budget $\xi$ξ. The higher $\xi$ξ is, the more privacy its reported location discloses. Accordingly, it receives higher payment from the platform as compensation. Besides, ULPT enables the platform to select a suitable set of winning workers to achieve desirable MCS service accuracy while taking into account of its budget limit and worker privacy requirements. For this purpose, a heuristic algorithm is devised with a bounded optimality gap. As formally proved in this manuscript, ULPT guarantees a series of nice properties, including $\xi$ξ-privacy, $(\alpha, \beta)$(α,β)-accuracy, budget feasibility. Moreover, both rigorous theoretical analysis and extensive simulations are conducted to evaluate tradeoffs among these three.

移动人群传感器（MCS）作为保证的数据收集范式，它利用无处不在的移动设备的功能在许多基于位置的敏感性任务中获取有关其周围环境的丰富信息。这些信息留下了一系列工人的历史记录，因此对其位置隐私构成了严重的威胁。单个工人可以以不同的方式感知位置，而不是遵循旨在掩盖用户隐私的传统解决方案，而是采用一种新颖的替代方法。平台。泄漏预算$ \ xi $ξ。为了实现其预算限制和工人隐私要求，赢得工人以实现理想的MC服务准确性。正式在此手稿中证明，ULPT保证了一系列不错的属性，包括$ \ xi $ξ-私人，$（\ alpha，\ beta）$（α，β） - 出现，准确性，预算可行性。进行了广泛的模拟，以评估这三个中的权衡。