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  4. Secure key generation using gait features for Body Sensor Networks
 
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Secure key generation using gait features for Body Sensor Networks
File(s)
Secure Key Generation Using Gait Features for Body Sensor Networks.pdf (759.98 KB)
Accepted version
Author(s)
Sun, Y
Wong, C
Yang, GZ
Lo, B
Type
Conference Paper
Abstract
With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.
Date Issued
2017-06-01
Date Acceptance
2017-05-30
Citation
2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017, 2017, pp.206-210
URI
http://hdl.handle.net/10044/1/53640
DOI
https://www.dx.doi.org/10.1109/BSN.2017.7936042
ISBN
9781509062447
Publisher
IEEE
Start Page
206
End Page
210
Journal / Book Title
2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Sponsor
Engineering & Physical Science Research Council (E
Grant Number
EP/N023242/1
Source
IEEE EMBS Annual International Body Sensor Networks Conference
Publication Status
Published
Start Date
2017-05-09
Finish Date
2017-05-12
Coverage Spatial
Eindhoven, Netherlands
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