Cross-Modality Continuous User Authentication and Device Pairing With Respiratory Patterns

• Published in: IEEE internet of things journal Vol. 10; no. 16; pp. 14197 - 14211
• Main Authors: Islam, Shekh M. M., Zheng, Yao, Pan, Yanjun, Millan, Marionne, Chang, Willy, Li, Ming, Boric-Lubecke, Olga, Lubecke, Victor, Sun, Wenhai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authentication; Belts; Biomedical monitoring; Biometrics; Breathing; Continuous authentication; continuous wave (CW) radar; Home environment; Independent component analysis; independent component analysis (ICA); Internet of Things; Jamming; joint approximate digaonalization of eigenmatrices; key derivation; Monitoring; noncontact sleep monitoring; Radar; Radar equipment; Security; Sleep apnea; Spoofing; telemedicine; test compliance
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3275099

At-home screening systems for obstructive sleep apnea (OSA) can bring convenience to remote chronic disease management. However, the unsupervised home environment is subject to spoofing and unintentional interference from the household member. To improve robustness, this work presents SIENNA, an insider-resistant breathing-based authentication/pairing protocol. SIENNA leverages the uniqueness of breathing patterns to automatically and continuously authenticate a user and pairs a mobile OSA app and a physiological monitoring radar system (PRMS). SIENNA does not require biometric enrollment and instead transforms the respiratory measurements taken during the user's routine physical checkup into breathing biometrics comparable with the PRMS readings. Furthermore, it can operate within a noisy multitarget home environment and is secure against a co-located attacker through the usage of joint approximate diagonalization of eignematric-independent component analysis, fuzzy commitment, and friendly jamming. We fully implemented SIENNA and evaluated its performance with medium-scale trials. Results show that SIENNA can achieve reliable <inline-formula> <tex-math notation="LaTeX">(>90 </tex-math></inline-formula>% success rate) user authentication and secure device pairing in a noisy environment against an attacker with full knowledge of the authorized user's breathing biometrics.