Authors:
Wei-Han Lee
1
;
Jorge Ortiz
2
;
Bongjun Ko
2
and
Ruby Lee
1
Affiliations:
1
Princeton University, United States
;
2
IBM Research, United States
Keyword(s):
Smartphone Sensors, Handwritten Pattern, Dynamic Timing Warping, Majority Voting.
Abstract:
Mobile devices including smartphones and wearable devices are increasingly gaining popularity as platforms
for collecting and sharing sensor data, such as the accelerometer, gyroscope, and rotation sensor. These
sensors are used to improve the convenience of smartphone users, e.g., supporting the mobile UI motion-based
commands. Although these motion sensors do not require users’ permissions, they still bring potential
risks of leaking users’ private information reflected by the changes of sensor readings. In this paper, we
investigate the feasibility of inferring a user’s handwritten pattern on a smartphone touchscreen by using the
embedded motion sensors. Specifically, our inference attack is composed of two key steps where we 1) first
exploit the dynamic time warping (DTW) technique to differentiate any pair of time-series sensor recordings
corresponding to different handwritten patterns; and 2) develop a novel sensor fusion mechanism to integrate
information contained in multiple
motion sensors by exploiting the majority voting strategy. Through extensive
experiments using real-world data sets, we demonstrate the effectiveness of our proposed attack which can
achieve 91.4% accuracy for inferring smartphone users’ handwritten patterns.
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