Technical Report: Insider-Resistant Context-Based Pairing for Multimodality Sleep Apnea Test
This addresses security risks in remote chronic disease management for patients using multimodal sleep apnea tests, though it appears incremental as it builds on existing techniques like JADE-ICA and fuzzy commitment.
The paper tackles the vulnerability of device pairing in at-home sleep apnea screening systems to insider attacks by presenting SIENNA, an insider-resistant context-based pairing protocol that achieves over 90% success rate in noisy environments and resists attackers with full context knowledge.
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 prevents 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.