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 as opposed to acquiring user's exact physical location. In this paper, we present PACL, which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set …
Read moreThe 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 as opposed to acquiring user's exact physical location. In this paper, we present PACL, which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes 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 can predict user's contextual location with the accuracy of around 87%. © 2014 IEEE.
