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On Inferring Browsing Activity on Smartphones via USB Power Analysis Side-Channel
Yang, Qing Gasti, Paolo Zhou, Gang Farajidavar, Aydin Balagani, Kiran S.
Yang, Qing
Gasti, Paolo
Zhou, Gang
Farajidavar, Aydin
Balagani, Kiran S.
Abstract
In this paper, we show that public USB charging stations pose a significant privacy risk to smartphone users even when no data communication is possible between the station and the user's mobile device. We present a side-channel attack that allows a charging station to identify which Webpages are loaded while the smartphone is charging. To evaluate this side-channel, we collected power traces of Alexa top 50 Websites on multiple smartphones under several conditions, including battery charging level, browser cache enabled/disabled, taps on the screen, Wi-Fi/LTE, TLS encryption enabled/disabled, time elapsed between collection of training and testing data, and location of the Website. The results of our evaluation show that the attack is highly successful: in many settings, we were able to achieve over 90% Webpage identification accuracy. On the other hand, our experiments also show that this side-channel is sensitive to some of the aforementioned conditions. For instance, when training and testing traces were collected 70 days apart, accuracies were as low as 2.2%. Although there are studies that show that power-based side-channels can predict browsing activity on laptops, this paper is unique, because it is the first to study this side-channel on smartphones, under smartphone-specific constraints. Further, we demonstrate that Websites can be correctly identified within a short time span of 2 x 6 seconds, which is in contrast with prior work, which uses 15-s traces. This is important, because users typically spend less than 15 s on a Webpage.
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2017-07-01
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On_Inferring.pdf
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Computer Science
DOI
https://doi.org/10.1109/TIFS.2016.2639446