If You Do Not Care About It, Sell It: Trading Location Privacy in Mobile Crowd Sensing

Mobile crowd sensing (MCS) is a technique where sensing tasks are outsourced to a crowd of mobile users. Since most of sensing tasks are location-dependent, workers are required to embed their locations into sensing reports, which incurs location privacy vulnerabilities. Realizing that workers perceive their location privacy differently, in this work we construct an auction-based trading market, facilitating location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform based on its own location privacy leakage budget $\xi$. The higher $\xi$ is, the less secrecy its reported location preserves. As a result, it receives higher payment from the platform as a compensation to its privacy loss. Besides, our mechanism enables the platform to select a suitable set of winning workers to achieve desirable service accuracy. For this purpose, a heuristic algorithm is devised, with polynomial-time complexity and bounded optimality gap. As formally proved in this manuscript, our proposed mechanism guarantees a series of nice properties, including $\xi$-privacy, $(\alpha,\beta)$accuracy, and budget feasibility.

移动群智感知（MCS）是一种将感知任务外包给一群移动用户的技术。由于大多数感知任务依赖于位置，因此要求工作者将其位置嵌入到感知报告中，这就导致了位置隐私易受侵犯。考虑到工作者对自身位置隐私的感知不同，在这项工作中我们构建了一个基于拍卖的交易市场，以促进工作者和平台之间的位置隐私交易。每个工作者可以根据自己的位置隐私泄露预算$\xi$决定向平台披露多少位置隐私。$\xi$越高，其报告位置所保留的保密性就越低。因此，它会从平台获得更高的报酬，作为对其隐私损失的补偿。此外，我们的机制使平台能够选择一组合适的中标工作者，以实现理想的服务精度。为此，设计了一种启发式算法，该算法具有多项式时间复杂度和有界最优性差距。正如本文正式证明的那样，我们提出的机制保证了一系列良好的特性，包括$\xi$-隐私性、$(\alpha,\beta)$准确性和预算可行性。