NDSS 2026 - Toward Practical Continuous Authentication on Wearable Devices via Wrist-Worn PPG

SESSION

NDSS Symposium 2026
23 February–27 February 2026, San Diego, California

PAPER
Know Me by My Pulse: Toward Practical Continuous Authentication on Wearable Devices via Wrist-Worn PPG
https://www.ndss-symposium.org/ndss-paper/know-me-by-my-pulse-toward-practical-continuous-authentication-on-wearable-devices-via-wrist-worn-ppg/

Biometric authentication using physiological signals offers a promising path toward secure and user-friendly access control in wearable devices. While electrocardiogram (ECG) signals have shown high discriminability, their intrusive sensing requirements and discontinuous acquisition limit practicality. Photoplethysmography (PPG), on the other hand, enables continuous, non-intrusive authentication with seamless integration into wrist-worn wearable devices. However, most prior work relies on high-frequency PPG (e.g., 75–500 Hz) and complex deep models, which incur significant energy and computational overhead—impeding deployment in power-constrained real-world systems. In this paper, we present the first real-world implementation and evaluation of a continuous authentication system on a smartwatch, We-Be Band, using low-frequency (25 Hz) multi-channel PPG signals. Our method employs a Bi-LSTM with attention mechanism to extract identity-specific features from short (4 s) windows of 4-channel PPG. Through extensive evaluations on both public datasets (PTTPPG) and our WeBe Dataset (26 subjects), we demonstrate strong classification performance with an average test accuracy of 88.11%, macro F1-score of 0.88, False Acceptance Rate (FAR) of 0.48%, False Rejection Rate (FRR) of 11.77%, and Equal Error Rate (EER) of 2.76%. Our 25 Hz system reduces sensor power consumption by 53% compared to 512 Hz and 19% compared to 128 Hz setups without compromising performance. We find that sampling at 25 Hz preserves authentication accuracy, whereas performance drops sharply at 20 Hz while offering only trivial additional power savings, underscoring 25 Hz as the practical lower bound. Additionally, we find that models trained exclusively on resting data fail under motion, while activity-diverse training improves robustness across physiological states.

AUTHORS
Wei Shao (University of California, Davis), Zequan Liang (University of California Davis), Ruoyu Zhang (University of California, Davis), Ruijie Fang (University of California, Davis), Ning Miao (University of California, Davis), Ehsan Kourkchi (University of California - Davis), Setareh Rafatirad (University of California, Davis), Houman Homayoun (University of California Davis), Chongzhou Fang (Rochester Institute of Technology)

ABOUT NDSS Symposium
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies. https://www.ndss-symposium.org/

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