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Title: Insider-Resistant Context-Based Pairing for Multimodality Sleep Apnea Test
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 noncompliant 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.
Authors:
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Award ID(s):
1662487
Publication Date:
NSF-PAR ID:
10312063
Journal Name:
IEEE Global Communications Conference: Communication & Information Systems Security - Communication & Information System Security
Sponsoring Org:
National Science Foundation
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