Privacy-aware contextual localization using network traffic analysis
| Autor: | Chen-Nee Chuah, Aveek K. Das, Parth H. Pathak, Prasant Mohapatra |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Traffic analysis
Computer Networks and Communications Computer science business.industry Bayesian network 020206 networking & telecommunications 02 engineering and technology Service provider World Wide Web 03 medical and health sciences 0302 clinical medicine Human–computer interaction 030220 oncology & carcinogenesis 0202 electrical engineering electronic engineering information engineering Wireless business Set (psychology) |
| Zdroj: | Das, AK; Pathak, PH; Chuah, CN; & Mohapatra, P. (2017). Privacy-aware contextual localization using network traffic analysis. Computer Networks, 118, 24-36. doi: 10.1016/j.comnet.2017.02.011. UC Davis: Retrieved from: http://www.escholarship.org/uc/item/5217k37s |
| ISSN: | 1389-1286 |
| DOI: | 10.1016/j.comnet.2017.02.011 |
| Popis: | © 2017 Elsevier B.V. The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery to user's 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 user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer) model, 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 100GB of total data. Our results show that PACL, when built using the Bayesian Network machine learning algorithm, can predict user's contextual location with the accuracy of around 89%. |
| Databáze: | OpenAIRE |
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