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Breathe-to-Pair (B2P): Respiration-Based Pairing Protocol for Wearable Devices

Authors:

Jafar Pourbemany,

Riccardo BettatiAuthors Info & Claims

WiSec '22: Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks

Pages 188 - 200

https://doi.org/10.1145/3507657.3528545

Published: 16 May 2022 Publication History

Abstract

We propose Breathe-to-Pair (B2P), a protocol for pairing and shared-key generation for wearable devices that leverages the wearer's respiration activity to ensure that the devices are part of the same body-area network. We assume that the devices exploit different types of sensors to extract and process the respiration signal. We illustrate B2P for the case of two devices that use respiratory inductance plethysmography (RIP) and accelerometer sensors, respectively. Allowing for different types of sensors in pairing allows us to include wearable devices that use a variety of different sensors. In practice, this form of sensor variety creates a number of challenges that limit the ability of the shared-key establishment algorithm to generate matching keys. The two main obstacles are the lack of synchronization across the devices and the need for correct noise-induced mismatches between the generated key bit-strings.

B2P addresses the synchronization challenge by utilizing Change Point Detection (CPD) to detect abrupt changes in the respiration signal and consider their occurrences as synchronizing points. Any potential mismatches are handled by optimal quantization and encoding of the respiration signal in order to maximize the mismatch correction rate and minimize the message overheads. Extensive evaluation on a dataset collected from 30 volunteers demonstrates that our protocol can generate a secure 256-bit key every 2.85 seconds (around one breathing cycle). Particular attention is given to secure B2P against device impersonation attacks.

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Cited By

Pourbemany JZhu YBettati R(2023)A Survey of Wearable Devices Pairing Based on Biometric SignalsIEEE Access10.1109/ACCESS.2023.325449911(26070-26085)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3254499
Mayet AAlizadeh SKakarash ZAl-Qahtani AAlanazi AAlhashimi HEftekhari-Zadeh ENazemi E(2022)Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow RegimeMathematics10.3390/math1010177010:10(1770)Online publication date: 23-May-2022
https://doi.org/10.3390/math10101770
Hong SKim YNam SPark K(2022)S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection SystemMathematics10.3390/math1003032810:3(328)Online publication date: 21-Jan-2022
https://doi.org/10.3390/math10030328
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Index Terms

Breathe-to-Pair (B2P): Respiration-Based Pairing Protocol for Wearable Devices
1. Security and privacy
  1. Cryptography
    1. Cryptanalysis and other attacks
    2. Key management
  2. Network security
    1. Mobile and wireless security
    2. Security protocols

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Published In

cover image ACM Conferences

WiSec '22: Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks

May 2022

314 pages

ISBN:9781450392167

DOI:10.1145/3507657

General Chair:
Murtuza Jadliwala
University of Texas at San Antonio, San Antonio, Texas, USA
,
Program Chairs:
Yongdae Kim
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
,
Alexandra Dmitrienko
University of Würzburg, Würzburg, Germany

Copyright © 2022 ACM.

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Published: 16 May 2022

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WiSec '22: 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks

May 16 - 19, 2022

TX, San Antonio, USA

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Cited By

Pourbemany JZhu YBettati R(2023)A Survey of Wearable Devices Pairing Based on Biometric SignalsIEEE Access10.1109/ACCESS.2023.325449911(26070-26085)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3254499
Mayet AAlizadeh SKakarash ZAl-Qahtani AAlanazi AAlhashimi HEftekhari-Zadeh ENazemi E(2022)Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow RegimeMathematics10.3390/math1010177010:10(1770)Online publication date: 23-May-2022
https://doi.org/10.3390/math10101770
Hong SKim YNam SPark K(2022)S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection SystemMathematics10.3390/math1003032810:3(328)Online publication date: 21-Jan-2022
https://doi.org/10.3390/math10030328
Mayet AAlizadeh SNurgalieva KHanus RNazemi ENarozhnyy I(2022)Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring SystemsEnergies10.3390/en1506198615:6(1986)Online publication date: 9-Mar-2022
https://doi.org/10.3390/en15061986
Hosseini STaylan OAbusurrah MAkilan TNazemi EEftekhari-Zadeh EBano FRoshani G(2021)Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical IndustriesPolymers10.3390/polym1321364713:21(3647)Online publication date: 23-Oct-2021
https://doi.org/10.3390/polym13213647
Taylan OAbusurrah MAmiri SNazemi EEftekhari-Zadeh ERoshani G(2021)Proposing an Intelligent Dual-Energy Radiation-Based System for Metering Scale Layer Thickness in Oil Pipelines Containing an Annular Regime of Three-Phase FlowMathematics10.3390/math91923919:19(2391)Online publication date: 26-Sep-2021
https://doi.org/10.3390/math9192391

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