The Novel Multi Source Method for the Randomness Extraction

Abstract

AbstractCryptography, statistical analysis, and numerical simulations are just a few of the areas where randomness is frequently applied. They are also a crucial resource in engineering and science. Usually, we have to supply unbiased, independent random bits for these applications. This raises the question of where these supposedly random bits can be found. Pseudorandom number generators are algorithms that produce seemingly random numbers but which are not actually random. When actual randomness is required, we employ true random number generators, which use unforeseen random events as their random source.Based on the inherent unpredictability of quantum measurements, quantum random number generators (QRNGs) produce actual random numbers. Sadly, due to classical noise, quantum randomness and classical randomness are invariably combined in practice. Also, randomness is frequently biased and correlated.The resulting raw bits sequence must be processed in order to provide output values of high quality that are as close to uniform distribution as is feasible. This calls for random extractors.We review numerous types of postprocessing as well as the randomness produced by quantum random number generators. The various randomness extractors are covered. Additionally, we employ information theoretically secure extractors and propose improved novel randomness extraction. Our new hybrid randomness extractor is based on a base of information-theoretically secure extractors. It uses multiple sources in the extraction process and can be changed based on our usage needs. Our extractor is resistant to quantum attacks, and the extraction process can be accelerated, say by means of parallelism.KeywordsQuantumQuantum cryptographyPostprocessingEntropyRandomness extractorsDeterministic extractorsSeeded extractors