Abstract
True Random Number Generators (TRNGs) are essential for cryptographic systems and communication security. According to the published standards, sufficient entropy derived from the stochastic model is required for TRNGs. Compared with the directly sampling jittery oscillating signal, the coherent sampling is a more efficient entropy extraction technique. In this paper, under the premise that the entropy per bit is sufficient, we focus on how to extract the entropy as much as possible from the coherent sampling in order to enhance the throughput of TRNGs. We provide a parameter adjustment method to maximize the generated entropy rate, and this method is based on our proposed stochastic model. According to the method, we design a TRNG architecture and implement it in Field Programmable Gate Arrays (FPGAs). In the experiment, the improved generation speed is up to 4 Mbps, and the output sequence is able to pass NIST SP 800-22 statistical tests without postprocessing. Compared to the basic coherent sampling, the bit generation rate is improved to 12 times.
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Acknowledgments
This work was partially supported by National Basic Research Program of China (973 Program No. 2013CB338001), Strategy Pilot Project of Chinese Academy of Sciences (No. XDA06010702) and National Natural Science Foundation of China (No. 61602476, No. 61402470).
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Yang, J., Ma, Y., Chen, T., Lin, J., Jing, J. (2017). Extracting More Entropy for TRNGs Based on Coherent Sampling. In: Deng, R., Weng, J., Ren, K., Yegneswaran, V. (eds) Security and Privacy in Communication Networks. SecureComm 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 198. Springer, Cham. https://doi.org/10.1007/978-3-319-59608-2_38
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DOI: https://doi.org/10.1007/978-3-319-59608-2_38
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