Improving data security with the utilization of matrix columnar transposition techniques

Abstract

The Graph Neural Network (GNN) is an advanced use of graph theory that is used to address complex network problems. The application of Graph Neural Networks allows the development of a network by the modification of weights associated with the vertices or edges of a graph G (V, E). Data encryption is a technique used to improve data security by encoding plain text into complex numerical configurations, hence minimizing the probability of data leaking. This study seeks to explain the potential of improving data security through the application of graph neural networks and transposition techniques for information manipulation. This study involves an algorithm and simulation that discusses the use of the transposition approach in manipulating information. This is accomplished by the implementation of a graph neural network, which develops the interaction between vertices and edges. The main result of this research shows empirical evidence supporting the notion that the length of the secret key and the number of characters utilized in data encryption have a direct impact on the complexity of the encryption process, hence influencing the overall security of the created data.