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Chizari, Hassan 
ORCID: 0000-0002-6253-1822, Lupu, Emil and Thomas, Paula
(2018)
Randomness of Physiological Signals in Generation Cryptographic
Key for Secure Communication Between Implantable Medical
Devices Inside The Body And The Outside World.
In: Living in the Internet of Things: Cybersecurity of the IoT 2018, 28-29 March, London, UK.
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Text (Peer reviewed version)
5739 Chizari (2018) Randomness of Physiological Signals.pdf - Accepted Version Available under License All Rights Reserved. Download (252kB) | Preview |
Abstract
A physiological signal must have a certain level of randomness inside it to be a good source of randomness for generating cryptographic key. Dependency to the history is one of the measures to examine the strength of a randomness source. In dependency to the history, the adversary has infinite access to the history of generated random bits from the source and wants to predict the next random number based on that. Although many physiological signals have been proposed in literature as good source of randomness, no dependency to history analysis has been carried out to examine this fact. In this paper, using a large dataset of physiological signals collected from PhysioNet, the dependency to history of Interpuls Interval (IPI), QRS Complex, and EEG signals (including Alpha, Beta, Delta, Gamma and Theta waves) were examined. The results showed that despite the general assumption that the physiological signals are random, all of them are weak sources of randomness with high dependency to their history. Among them, Alpha wave of EEG signal shows a much better randomness and is a good candidate for post-processing and randomness extraction algorithm.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | isbn 978-1-78561-843-7 ©2018 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. |
| Uncontrolled Keywords: | Randomness; Entropy; Santha-Vazirani Source; Implants; Physiological Signals |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Susan Turner |
| Date Deposited: | 25 Jun 2018 13:01 |
| Last Modified: | 31 Aug 2023 08:01 |
| URI: | https://eprints.glos.ac.uk/id/eprint/5739 |
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