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.
Original language | English |
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Pages (from-to) | 126-138 |
Number of pages | 13 |
Journal | Future Generation Computer Systems |
Volume | 84 |
Early online date | 26 Oct 2017 |
DOIs | |
Publication status | Published - Jul 2018 |
Keywords
- Lightweight
- Key management
- Real-time
- Body sensor networks
- Embedded devices
Profiles
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Edwin Ren
- School of Computing Sciences - Associate Professor in Computing Sciences
- Cyber Security Privacy and Trust Laboratory - Member
- Data Science and AI - Member
- Future Oriented Resilient and Intelligent Networks Group - Member
- Smart Emerging Technologies - Member
Person: Research Group Member, Academic, Teaching & Research