Protecting Smartphone Screen Notification Privacy by Verifying the Gripping Hand

Abstract

As the most common personal devices, smartphones contain the user's private information. While people use mobile devices anytime and anywhere, the sensitive contents might be leaked from the screens. The smartphone notifications cause such privacy leakages even on a lock screen. With the aim to alert the user of an event (e.g., text messages, phone calls and calendar reminders), these onscreen notifications usually contain the sender's name and even a clip of the contents for preview. Such information, if not displayed appropriately, may cause the leakages of the user's social relations, personal hobbies and private message contents. This work focuses on wisely displaying the notifications to avoid leaking the user's privacy. We develop an unobtrusive user authentication system to confirm the user identity via their gripping-hands before displaying notifications. In particular, we carefully design an inaudible acoustic signal and emit it from the smartphone speaker to sense the gripping hand, when there is a need to push notifications. The signal propagating to the smartphone's microphones carries the user's biometric information related to the gripping hand (e.g., palm size and gripping strength). We further derive the Mel Frequency Cepstral Coefficient time series and develop a machine learning-based algorithm to identify the user. The experimental results show that our system can identify 8 users with 92% accuracy.