We present a true random number generator (TRNG) using dark noise of a CMOS image sensor. Because the proposed TRNG is based on the dark characteristics of the CMOS image sensor, it does not require any additional hardware, such as light source and optics, for providing true randomness. Therefore, it can be a promising solution for compact and low-cost mobile application. By using NIST SP 800-90B entropy assessment suite, we evaluate the min-entropy for the raw outputs of our original noise source and the final random numbers including post-processing as well. We also adopt NIST SP 800-22 statistical randomness test suite for the evaluation of the random numbers. The test results demonstrate that the generated random numbers pass all the statistical tests and have high entropy. INDEX TERMS Random number generation, CMOS image sensors, dark current.
SP 800-90B of NIST(USA) and AIS.31 of BSI(Germany) are representative statistical tests for TRNGs. In this paper, we concentrate on AIS.31 which is under the ongoing international standardization process. We examine the probabilistic meaning of each statistic of the test in AIS.31 and investigate its probability distribution. By changing significance level and the length of sample bits, we obtain formalized accept region of the test. Furthermore we propose the accept regions for some iterative tests, that are not mentioned in AIS.31, and provide some simulations.
For a secure communication system, it is necessary to use secure cryptographic algorithms and keys. Modern cryptographic system generates high entropy encryption key through standard key derivation functions. Using recent progress in quantum key distribution(QKD) based on quantum physics, it is expected that we can enhance the security of modern cryptosystem. In this respect, the study on the dual key agreement is required, which combines quantum and modern cryptography. In this paper, we propose two key derivation functions using dual key agreement based on QKD and RSA cryptographic system. Furthermore, we demonstrate several simulations that estimate entropy of derived key so as to support the design rationale of our key derivation functions.
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