Contextual localization through network traffic analysis
Contextual localization through network traffic analysis
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DOI：
10.1109/infocom.2014.6848021
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Aveek K. Das;Parth H. Pathak;Chen-Nee Chuah;P. Mohapatra
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文献类型：
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作者：
Aveek K. Das;Parth H. Pathak;Chen-Nee Chuah;P. Mohapatra
基于位置的服务的兴起为内容服务提供商提供了许多机会来优化基于用户位置的内容交付。由于共享精确位置仍然是用户之间的主要隐私问题，因此许多基于位置的服务依赖于上下文位置（例如，住宅、咖啡馆等）。而不是获取用户的确切物理位置。在本文中，我们提出了PACL（隐私感知上下文定位器），它可以学习用户的上下文位置，只需被动地监测用户的网络流量。PACL可以从用户的网络流量中识别一组重要属性（统计和基于应用的），并以非常高的准确度预测用户的上下文位置。我们设计和评估PACL使用超过1700个用户的真实网络跟踪超过100千兆字节的总数据。我们的研究结果表明，PACL（使用决策树构建）可以预测用户的上下文位置的准确率约为87%。
The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery based on user's location. Since sharing precise location remains a major privacy concern among the users, many location-based services rely on contextual location (e.g. residence, cafe etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer), which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100 gigabytes of total data. Our results show that PACL (built using decision tree) can predict user's contextual location with the accuracy of around 87%.