TY - GEN
T1 - Insider-Resistant Context-Based Pairing for Multimodality Sleep Apnea Test
AU - Zheng, Yao
AU - Islam, Shekh Md Mahmudul
AU - Pan, Yanjun
AU - Millan, Marionne
AU - Aggelopoulos, Samson
AU - Lu, Brian
AU - Yang, Alvin
AU - Yang, Thomas
AU - Aelmore, Stephanie
AU - Chang, Willy
AU - Power, Alana
AU - Li, Ming
AU - Boric-Lubecke, Olga
AU - Lubecke, Victor
AU - Sun, Wenhai
N1 - Funding Information:
VIII. ACKNOWLEDGEMENT This work is partly supported by NSF grants CNS-1948568, DGE-1662487, IIP-1831303, IIS-1915738, ARO grant W911NF-19-1-0050 as well as the equipment from TMYTEK mmWave research initiative.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The increasingly sophisticated at-home screening systems for obstructive sleep apnea (OSA), integrated with both contactless and contact-based sensing modalities, bring convenience and reliability to remote chronic disease management. However, the device pairing processes between system components are vulnerable to wireless exploitation from a non-compliant user wishing to manipulate the test results. This work presents SIENNA, an insider-resistant context-based pairing protocol. SIENNA leverages JADE-ICA to uniquely identify a user's respiration pattern within a multi-person environment and fuzzy commitment for automatic device pairing, while using friendly jamming technique to prevent an insider with knowledge of respiration patterns from acquiring the pairing key. Our analysis and test results show that SIENNA can achieve reliable (> 90% success rate) device pairing under a noisy environment and is robust against the attacker with full knowledge of the context information.
AB - The increasingly sophisticated at-home screening systems for obstructive sleep apnea (OSA), integrated with both contactless and contact-based sensing modalities, bring convenience and reliability to remote chronic disease management. However, the device pairing processes between system components are vulnerable to wireless exploitation from a non-compliant user wishing to manipulate the test results. This work presents SIENNA, an insider-resistant context-based pairing protocol. SIENNA leverages JADE-ICA to uniquely identify a user's respiration pattern within a multi-person environment and fuzzy commitment for automatic device pairing, while using friendly jamming technique to prevent an insider with knowledge of respiration patterns from acquiring the pairing key. Our analysis and test results show that SIENNA can achieve reliable (> 90% success rate) device pairing under a noisy environment and is robust against the attacker with full knowledge of the context information.
UR - http://www.scopus.com/inward/record.url?scp=85127289675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127289675&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM46510.2021.9685852
DO - 10.1109/GLOBECOM46510.2021.9685852
M3 - Conference contribution
AN - SCOPUS:85127289675
T3 - 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
BT - 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021
Y2 - 7 December 2021 through 11 December 2021
ER -