Conference Proceeding Article
Due to numerous benefits, sensor-rich smartwatchesand wrist-worn wearable devices are quickly gaining popularity.The popularity of these devices also raises privacy concerns. Inthis paper we explore one such privacy concern: the possibility ofextracting the location of a user’s touch-event on a smartphone,using the inertial sensor data of a smartwatch worn by the useron the same arm. This is a major concern not only because itmight be possible for an attacker to extract private and sensitiveinformation from the inputs provided but also because the attackmode utilises a device (smartwatch) that is distinct from thedevice being attacked (smartphone). Through a user study wefind that such attacks are possible. Specifically, we can infer theuser’s entry pattern on a qwerty keyboard, with an error boundof ±2 neighboring keys, with 73.85% accuracy. As a possiblepreventive mechanism, we also show that adding a little whitenoise to inertial sensor data can reduce the inference accuracyby almost 30%, without affecting the accuracy of macro-gesturerecognition.
Science and Technology Studies | Technology and Innovation
Software and Cyber-Physical Systems
2017 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Kailua-Kona, USA, 2017 March 13-17
City or Country
SOUGATA SEN; GROVER, Karan; SUBBARAJU, Vigneshwaran; and MISRA, Archan.
Inferring smartphone keypress via smartwatch inertial sensing. (2017). 2017 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Kailua-Kona, USA, 2017 March 13-17. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3583
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