True random number generator

Abstract

The present invention discloses a true random number generator, which adopts an SP structure. The true random number generator comprises: n physical random sources, n digital processing modules, a diffusion layer, an aliasing layer and a post-processing module. The n physical random sources are used to generate n physical signals; the n digital processing modules are used to respectively match andreceive the n physical signals, and convert the n physical signals into n digital signals; the diffusion layer is used to receive the n digital signals, perform an exclusive-OR operation on the n digital signals, and output n-channel signals, wherein each-channel signals comprise physical signals of at least three physical random sources; the aliasing layer is used to receive the n-channel signals, mix the n-channel signals to realize repeated mix among the n-channel signals, and output mix results; and the post-processing module is used to receive the mix results output by the aliasing layer, and eliminate the deviation and dependence of the mix results. Thus, according to the random number generator disclosed by the present invention, the robustness of the random number generator can beimproved.

Description

technical field [0001] The invention relates to a true random number generator with SP (Substitution Permutation) structure. Background technique [0002] A stochastic process is a repetitive process whose output is not easy to find a deterministic pattern to describe. A good random number generator should work efficiently, which means it must be able to generate a large number of random numbers in a short time. Random numbers are widely used in many fields, such as cryptography, spread spectrum communication, digital simulation, information security, random simulation and so on. So how to generate good random numbers is crucial. [0003] Currently, there are generally two types of generators that generate random sequences: true random number generators and pseudo-random number generators. A pseudo-random number generator needs a seed and some deterministic algorithm to generate a random sequence. A true random number generator, on the other hand, utilizes a non-determin...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a true random number generator, which can solve the problem that the physical random source is vulnerable to attacks, and improves the robustness of the random number generator

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