Privacy-aware contextual localization using network traffic analysis
The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery to users wireless devices based on her location. Since the sharing precise location remains a major privacy concern among the users, certain location-based services rely on contextual location (e.g. residence, work, etc.) as opposed to acquiring users exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer) model, which can learn users contextual location just by passively monitoring users network traffic. PACL can discern a set of vital attributes (statistical and application-based) from users network traffic, and predict users contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100GB of total data. Our results show that PACL, when built using the Bayesian Network machine learning algorithm, can predict users contextual location with the accuracy of around 89%.