利用便携式电脑中的熵源生成真正的随机数

Rahul M. Koushik, Aravind Perichiappan, H. Om, Abhishek Banerji, Sivaraman Eswaran, Prasad B. Honnavalli
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引用次数: 1

摘要

随机数在各个领域都有广泛的应用,如密码学、机器学习和人工智能模拟中初始权重的随机化、蒙特卡罗计算、工业测试、计算机游戏和赌博。只有熵源才有可能产生随机数。真正的随机数发生器(TRNG)使用物理熵源生成随机数。TRNG 的随机性可以用科学方法来描述和测量。TRNG 的缺点是通常需要一个包含熵物理源的外部硬件设备。通过尝试使用已成为设备环境一部分的熵源,可以消除这种必要性。这项工作试图识别此类源并分析其熵水平。然后,利用确定的熵源建立一个可用于生成真正随机数的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generation of True Random Numbers using Entropy Sources Present within Portable Computers
Random numbers have wide-ranging applications in various domains such as cryptography, randomization of initial weights in machine learning and AI-simulation, Monte Carlo computation, industrial testing, computer games, gambling. The generation of random numbers is only possible from a sourceof entropy. A true random number generator (TRNG) uses a physical source of entropy to generate random numbers. The randomness of a TRNG can be scientifically characterized, and measured. A drawback of TRNGs is that they usually need an external hardware device containing the physical source of entropy. This necessity can be eliminated by attempting to use sources that are already part of the device's environment. This work attempts to identify such sources and analyze their entropy levels. The identified sources of entropy are then used to build a model that can be used to generate truly random numbers.
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