Sequence Alignment Algorithm for Statistical Similarity Assessment

TL;DR

This paper introduces a sequence alignment algorithm for assessing statistical similarity that interprets sequence dependence through two parameters, enabling easy implementation and online testing with real data from hardware random number generators.

Contribution

A novel sequence alignment algorithm for statistical similarity assessment that is simple to interpret, implement, and suitable for online testing with hardware-generated data.

Findings

Verified with real hardware random number data

High-cost alignments indicate significant sequence differences

Low-cost alignments suggest sequence dependence

Abstract

This paper presents a new approach to statistical similarity assessment based on sequence alignment. The algorithm performs mutual matching of two random sequences by successively searching for common elements and by applying sequence breaks to matchless elements in the function of exponential cost. As a result, sequences varying significantly generate a high-cost alignment, while for low-cost sequences the introduced interruptions allow inferring the nature of sequences dependence. The most important advantage of the algorithm is an easy interpretation of the obtained results based on two parameters: stretch ratio and stretch cost. The operation of the method has been simulation tested and verified with the use of real data obtained from hardware random number generators. The proposed solution ensures simple implementation enabling the integration of hardware solutions, and operation based on only two sequences of any length predisposes the method to online testing.