Cross-platform transformation method for pseudo random number generator in FTS random algorithm

Abstract

The invention discloses a cross-platform transformation method for a pseudo random number generator in an FTS random algorithm. The cross-platform transformation method comprises the steps: recombining weights corresponding to User0-UserY into a Merkle tree; starting from the root node of the merkle tree, using a random seed as a random source, and using a pseudo random number generator to generate a random number which is less than the node weight of the current tree; taking the random seed X as an initialization seed, and selecting leaf nodes; performing transformation; performing verification; based on a safe random seed, a cross-platform pseudo-random number generator is constructed by adopting a hash algorithm and a large number modulo operation, so that the universality of algorithm transplantation is realized, the algorithm does not depend on any development language and a basic library of an operating system any more in the operation process, and the cross-platform pseudo-random number generator is constructed by using universal mathematical logic; according to the method, different development languages and operating systems can be universally realized, and the same seed is ensured to obtain a consistency result after cross-platform, so that the error probability can be reduced, and the universality of protocol realization can be ensured.

Description

technical field [0001] The invention relates to the field of random selection, in particular to a cross-platform transformation method for a pseudo-random number generator in an FTS random algorithm. Background technique [0002] The pseudo-random number is a sequence of random numbers from [0,1] uniform distribution calculated by a deterministic algorithm. Not really random, but has statistical characteristics similar to random numbers, such as uniformity, independence, etc. When calculating the pseudo-random number, if the initial value (seed) used remains unchanged, the sequence of the pseudo-random number will also remain unchanged. Pseudo-random numbers can be generated by computers in large quantities. In order to improve simulation efficiency in simulation research, pseudo-random numbers are generally used instead of real random numbers. The pseudo-random numbers that have a very long cycle period and can pass the random number test are generally used in the simulat...

AI Technical Summary

Problems solved by technology

In the selection of the pseudo-random number generator, the existing technology uses the security-based random seed that comes with the development language library to select random numbers within a certain interval. Different development languages ​​(java / c++ / go) have similar Pseudo-random number generator implementation, because it is difficult to ensure that the principle is consistent, the same security seed is difficult to ensure that the program running results are consistent across platforms

Images