DUCK Experiment Publications

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The development and construction of DUCK detector system is supported by NSF LEAPS-MPS Award 2316097

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2024-04 HRNG preprint arxiv 2404.09395.pdf

Data Analysis Methods Preliminaries for a Photon-based Hardware Random Number Generator

cite as: Dmitriy Beznosko, Keith Driscoll, Fernando Guadarrama, Steven Mai, and Nikolas Thornton. "Data Analysis Methods Preliminaries for a Photon-based Hardware Random Number Generator", arXiv:2404.09395 [cs.CR], 2024/04 https://arxiv.org/abs/2404.09395

Abstract. High quality random numbers are necessary in the modern world. Ranging from encryption keys in cyber security to models and simulations for scientific use: it’s important that these random numbers are of high quality and quickly attainable. One common solution to the generation of random numbers is that of pseudo-random number generators, or PRNGs. PRNGs generate random numbers by first quantifying some unpredictable phenomena into a number or string and feeding it into an algorithm which yields numbers randomly based on that “seed”. Easy places to find seeds include the user’s mouse movements or the machine’s uptime. These are only pseudorandom, however, as if given the same seed twice, the PRNG would generate the same “random” output. This is great for games like Minecraft, but not so great for cybersecurity encryption key generation. By using a hardware random number generator (HRNG), random numbers that are not susceptible to the flaws found in PRNGs can be attained at a high rate.