Szczepanski, J and Wajnryb, E and Amigo, JM and Sanchez-Vives, MV and Slater, M (2004) Biometric random number generators. COMPUT SECUR , 23 (1) 77 - 84. 10.1016/S0167-4048(03)00002-6.
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Up to now biometric methods have been used in cryptography for authentication purposes. In this paper we propose to use biological data for generating sequences of random bits. We point out that this new approach could be particularly useful to generate seeds for pseudo-random number generators and so-called "key sessions". Our method is very simple and is based on the observation that, for typical biometric readings, the last binary digits fluctuate "randomly". We apply our method to two data sets, the first based on animal neurophysiological brain responses and the second on human galvanic skin response. For comparison we also test our approach on numerical samplings of the Ornstein-Uhlenbeck stochastic process. To verify the randomness of the sequences generated, we apply the standard suite of statistical tests (FIPS 140-2) recommended by the National Institute of Standard and Technology for studying the quality of the physical random number generators, especially those implemented in cryptographic modules. Additionally, to confirm the high cryptographic quality of the biometric generators, we also use the often recommended Maurer's universal test and the Lempel-Ziv complexity test, which estimate the entropy of the source. The results of all these verifications show that, after appropriate choice of encoding and experimental parameters, the sequences obtained exhibit excellent statistical properties, which opens the possibility of a new design technology for true random number generators. It remains a challenge to find appropriate biological phenomena characterized by easy accessibility, fast sampling rate, high accuracy of measurement and variability of sampling rate. (C) 2004 Elsevier Ltd. All rights reserved.
|Title:||Biometric random number generators|
|Keywords:||biometric methods, random number generator, stochastic process, statistical tests, computer communication protocols|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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