International audienceSecurity in true random number generation in cryptography is based on entropy per bit at the generator output. The entropy is evaluated using stochastic models. Several recent works propose stochastic models based on assumptions related to selected physical analog phenomena such as noise or jittery signal and on the knowledge of the principle of randomness extraction from the obtained analog signal. However, these assumptions simplify often considerably the underlying analog processes, which include several noise sources. In this paper, we present a new comprehensive multilevel approach, which enables to build the stochastic model based on detailed analysis of noise sources starting at transistor level and on conversion of the noise to the clock jitter exploited at the generator level. Using this approach, we can estimate proportion of the jitter coming only from the thermal noise, which is included in the total clock jitter

Security in true random number generation in cryptography is based on entropy per bit at the generator output. The entropy is evaluated using stochastic models. Several recent works propose stochastic models based on assumptions related to selected physical analog phenomena such as noise or jittery signal and on the knowledge of the principle of randomness extraction from the obtained analog signal. However, these assumptions simplify often considerably the underlying analog processes, which include several noise sources. In this paper, we present a new comprehensive multilevel approach, which enables to build the stochastic model based on detailed analysis of noise sources starting at transistor level and on conversion of the noise to the clock jitter exploited at the generator level. Using this approach, we can estimate proportion of the jitter coming only from the thermal noise, which is included in the total clock jitter.

Abstract. Security in random number generation for cryptography is closely related to the entropy rate at the generator output. This rate has to be evaluated using an appropriate stochastic model. The stochastic model proposed in this paper is dedicated to the transition effect ring oscillator (TERO) based true random number generator (TRNG) proposed by Varchola and Drutarovsky in 2010. The advantage and originality of this model is that it is derived from a physical model based on a detailed study and on the precise electrical description of the noisy physical phenomena that contribute to the generation of random numbers. We compare the proposed electrical description with data generated in a 28 nm CMOS ASIC implementation. Our experimental results are in very good agreement with those obtained with both the physical model of TERO's noisy behavior and with the stochastic model of the TERO TRNG, which we also confirmed using the AIS 31 test suites.