Knight Pseudorandom Walk Manifold for Scrambling Multidimensional Data

Authors

DOI:

https://doi.org/10.31558/2786-9482.2024.1.1

Keywords:

data scrambling, knight pseudorandom walk, break-in probability, similarity rate

Abstract

The knight open tour problem is to build a sequence of knight moves covering a chessboard completely, without repetitions, where the starting position and ending position are always different. A solution of the knight open tour problem is similar to a sequence of pseudorandom numbers used to map data into non-readable yet usable information. The knight open tour problem has a manifold of solutions for a starting position of the knight depending on the chessboard size. Solutions of the knight open tour problem, which appear like a random series of knight positions, i. e. a pseudorandom walk, are used to further improve balance of the scrambling simplicity and productivity, where the main indicators are the break-in probability and similarity index. The break-in probability is dramatically decreased by taking into account a knight pseudorandom walk manifold for a starting position on a given chessboard. A pessimistic estimation of the break-in probability for an 8×8 chessboard is less than 10-16. A similarly expected estimation for an 8×8×8 chessboard is less than 10-26. A distinct knight pseudorandom walk (out of a manifold of pseudorandom walks) is built online by a given seed integer for a pseudorandom number generator. The scrambled data vector is built online as well in linear runtime complexity. Meanwhile, the similarity index is acceptable, rapidly dropping as the chessboard size is increased (for bigger multidimensional data arrays). A knight pseudorandom walk is determined by the chessboard size, the starting position, the way to vectorize the knight pseudorandom walk, and the pseudorandom number generator seed allowing to specifically move the knight onward through situations with multiple possible moves of the knight. The knight-open-tour scrambler has  1016 to  1021 times lower break-in probability compared to an ordinary pseudorandom number generator, depending on the chessboard size and the starting position of the knight.

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Published

2024-10-10

How to Cite

[1]
Романюк, В. 2024. Knight Pseudorandom Walk Manifold for Scrambling Multidimensional Data. Ukrainian Journal of Information Systems and Data Science. 1 (Oct. 2024), 1–13. DOI:https://doi.org/10.31558/2786-9482.2024.1.1.