True random number generator

Abstract

The invention discloses a true random number generator. The true random number generator comprises a high-frequency clock generator, a low-frequency clock generator, a noise scrambling circuit and a sampling circuit. The high-frequency clock generator is used for generating high-frequency clock signals. The low-frequency clock generator is used for generating low-frequency clock signals. The noise scrambling circuit is used for generating noise signals and coupling the noise signals to the low-frequency clock generator, and the low-frequency clock generator outputs low-frequency clock signals coupled with the noise signals. The sampling circuit is used for sampling high-frequency clock signals according to the low-frequency clock signals coupled with the noise signals to generate true random number signals. By means of the mode, the true random number signals high in randomness can be generated in a relatively simple mode and with the lower cost.

Description

technical field [0001] The invention relates to the field of information security, in particular to a true random number generator. Background technique [0002] With the rapid development of computer technology and communication technology, especially the widespread use of the Internet, information has become a very important asset in today's society. With the continuous development of the information society, the demand for information security is also increasing. Among them, cryptography is an important part of information security, and the random number generator (Random Number Generator, RNG) plays a very important role in the field of cryptography. It is an important part of the hardware implementation of the cryptosystem. Specifically, the random number generator is used to generate high-quality random number sequences to encrypt the information itself and the transmission process of the information, so as to realize the confidential transmission of information. [...

AI Technical Summary

Problems solved by technology

Among them, the oscillation sampling method is the most widely used due to its simple implementation method and good data randomness, but the random number obtained by the oscillation sampling method is poor in randomness.

In order to improve the randomness of the random numbers generated by the true random number generator based on the oscillation sampling method, there are currently two solutions: one is to reduce the frequency of

Images