Authors’ copy downloaded from: https://sprite.utsa.edu/ Copyright may be reserved by the publisher. deWristified: Handwriting Inference Using Wrist-Based Motion Sensors Revisited Raveen Wijewickrama Anindya Maiti Murtuza Jadliwala University of Texas at San Antonio University of Texas at San Antonio University of Texas at San Antonio
[email protected] [email protected] [email protected] ABSTRACT ever since the inception of commercial, consumer-grade wearable Several recent research efforts have shown that privacy of handwrit- devices such as smart watches and fitness bands. Several proposals ten information is vulnerable to inference threats that employ zero- in the research literature have already demonstrated how data permission motion sensors commonly found on wrist-wearables from zero-permission wrist-wearable sensors can be abused to (e.g., smart watches and fitness bands) as information side-channels. infer keystrokes, user-activities, and behavior [11, 13–16, 19, 23, While the adversary model in these earlier efforts have been reason- 25, 26, 28, 29]. In the same vein, multiple research efforts have also able and the proposed inference (or threat) frameworks themselves demonstrated the feasibility of inferring handwritten text using are practical and have technical merit, the related empirical eval- motion sensors (such as accelerometers and gyroscopes) present uations suffer from several significant shortcomings, such as,use onboard these wrist-wearables. Some of the initial efforts in this of specialized sensor hardware and highly constrained or restric- direction showed the feasibility of inferring larger handwriting tive experimental procedures, to name a few. As a result, it is hard gestures, such as, writing on a whiteboard [6] or using hand/finger to estimate the practical feasibility of these threats from existing movements to write in the air [4, 5, 31].