Chen, Zitao, Ren, Wei, Ren, Yi ORCID: https://orcid.org/0000-0001-7423-6719 and Choo, Kim-Kwang Raymond (2018) LiReK: A lightweight and real-time key establishment scheme for wearable embedded devices by gestures or motions. Future Generation Computer Systems, 84. pp. 126-138. ISSN 0167-739X
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Abstract
With the recent trend in wearable technology adoption, the security of these wearable devices has been the subject of scrutiny. Traditional cryptographic schemes such as key establishment schemes are not practical for deployment on the (resource-constrained) wearable devices, due to the limitations in their computational capabilities (e.g. limited battery life). Thus, in this study, we propose a lightweight and real-time key establishment scheme for wearable devices by leveraging the integrated accelerometer. Specifically, we introduce a novel way for users to initialize a shared key using random shakes/movements on their wearable devices. Construction of the real-time key is based on the users’ motion (e.g. walking), which does not require the data source for key construction in different devices worn by the same user to be matching. To address the known limitations on the regularity and predictability of gait, we propose a new quantization method to select data that involve noise and uncertain factors when generating secure random number. This enhances the security of the derived key. Our evaluations demonstrate that the matching rate of the shake-to-generate secret key is up to 91.00% and the corresponding generation rate is 2.027 bit/s, and devices worn on human participant’s chest, waist, wrist and carried in the participant’s pocket can generate 4.405, 4.089, 6.089 and 3.204 bits random number per second for key generation, respectively.
Item Type: | Article |
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Uncontrolled Keywords: | lightweight,key management,real-time,body sensor networks,embedded devices |
Faculty \ School: | Faculty of Science > School of Computing Sciences |
UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI |
Related URLs: | |
Depositing User: | Pure Connector |
Date Deposited: | 16 Nov 2017 06:06 |
Last Modified: | 10 Dec 2024 01:30 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/65466 |
DOI: | 10.1016/j.future.2017.10.008 |
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