Min-Entropy Estimation for Semiconductor Superlattice True Random Number Generators

Abstract Semiconductor superlattice true random number generator (SSL-TRNG) has an outstanding practical property to serve as high-throughput and high-security cryptographic applications. Security in random number generators is closely related to the min-entropy of the raw output because feeding cryptographic applications with insufficient entropy leads to poor security and vulnerability to malicious attacks. However, no research has focused on the minimum entropy estimation based on the random model for SSL-TRNG, which is a highly recommended method for evaluating the security of a specific TRNG structure. This paper proposes a min-entropy estimation method for the SSL-TRNG by extending the Markov stochastic model derived from the memory effects. By calculating the boundary of the transition matrix, the min-entropy result is that the average value of each sample (1 bit) is 0.2487. Moreover, we demonstrate that the estimator is accurate enough to adjust compression rate dynamically in post-processing to reach the required security level, estimating entropy on the fly rather than off-line.