Reducing Wind Direction Data for the Unpredictable Random Number Generator based on Linear Feedback Shift Register

Abstract

Chiba and Ichikawa (2023) proposed an unpredictable random number generator (URNG) that samples a linear feedback shift register (LFSR) at varying intervals fluctuated by weather data. Although their URNG meets the criteria of the Diehard and NIST tests, it requires a substantial amount of weather data. This study examines various design options to reduce wind direction data. Our results show that the resulting random sequence passes the Diehard and NIST tests under the following conditions: (1) use of a 64-bit or longer LFSR, (2) incorporation of a robust hash function (e.g., MD5) for data selection, and (3) adoption of 64 or greater number of observations with sufficient entropy.