A Depth-Based DES Model for Enhanced Confidentiality in Datasets

Data encryption is a fundamental aspect of ensuring the confidentiality and security of sensitive information in various applications. In this paper, we present an implementation of the Data Encryption Standard (DES) model for encrypting datasets, aiming to enhance data security through multilevel depth encryption with dynamic key transformation. The DES algorithm, known for its robustness and efficiency, forms the basis of our encryption framework. We explore novel encryption techniques by applying the DES algorithm at multiple levels of depth, leveraging dynamic key transformation strategies to enhance security. Specifically, we introduce multiple levels of depth encryptions through the use of random numbers that determine the number of key transformations. The key transformation process dynamically modifies the encryption key to provide additional layers of security. Our initial experimental results in particular CPU time/speed utilization and RAM demands demonstrate the effectiveness of our approach in enhancing data security. By combining the robustness of the DES algorithm with innovative encryption techniques, we achieve a higher level of protection for sensitive information, ensuring its confidentiality.