移动位置服务中的隐私暴露问题
The Privacy Exposure Problem in Mobile Location-based Services
摘要：
移动位置服务（LBS）是由移动众包提供的基于用户位置的上下文感知智能服务。由于智能手机能够收集和传播大量的用户位置嵌入式传感信息，移动用户的隐私保护已成为一个至关重要的问题。本文提出了一个度量隐私暴露量化的概念，这是主观的和定性的性质，以支持移动LBS评估隐私保护解决方案的有效性。该指标结合了活动覆盖率和活动一致性，以解决两个主要的隐私威胁，即活动热点披露和活动转换披露。此外，我们提出了一种算法来最大限度地减少移动LBS的隐私暴露。我们通过大量的模拟评估所提出的度量和隐私保护传感算法。此外，我们还实现了基于Android的移动系统中的算法，并进行了真实世界的实验。我们的模拟和实验结果表明，（1）所提出的度量可以适当地量化在空间域中的人类活动的隐私暴露水平和（2）所提出的算法可以有效地掩盖用户的活动热点和过渡在高和低用户移动水平。
英文摘要：
Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context-aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels.
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