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 8x8 chessboard is less than
10-16 . A similarly expected estimation for an 8x8x8 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-opentour 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.