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Randomness of physiological signals in generation cryptographic key for secure communication between implantable medical devices inside the body and the outside world

机译:生成加密密钥的生理信号随机性,用于在身体和外界内部植入医疗器械之间的安全通信

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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.
机译:生理信号必须具有一定程度的随机性,它是产生加密密钥的良好随机性源。依赖历史是检查随机性源强度的措施之一。在依赖历史中,对手具有无限访问来自源的生成随机位的历史,并且想要基于该来预测下一个随机数。虽然在文学中提出了许多生理信号,但由于随机性的好来源,也没有对历史分析进行依赖以检查这一事实。本文使用从物理体收集的生理信号的大数据集,对时介间隔(IPI),QRS复合物和脑电图(包括α,β,δ,γ和γ波)的依赖性。结果表明,尽管生理信号是随机的一般假设,但所有这些都是随机性弱的随机性,对其历史具有高依赖性。其中,EEG信号的alpha波显示出更好的随机性,并且是后处理和随机性提取算法的良好候选者。

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