LR-Auth: Towards Practical Implementation of Implicit User Authentication on Earbuds

The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific templates through linear correlations between two audio streams instead of complex machine-learning models. Our prototype, evaluated with 30 subjects under diverse conditions, demonstrates over 99% balanced accuracy with five 100 ms audio segments, even in noisy environments and during music playback. LR-Auth significantly reduces system overhead, achieving a 20 × to 404 × decrease in latency and a 24 × to 410 × decrease in energy consumption compared to existing methods. These results highlight LR-Auth's potential for accurate, robust, and efficient user authentication on resource-constrained earbuds.