Implementation and Evaluation of Data Protection in Databases Using Symmetric Encryption Algorithms

Abstract

In an era marked by rising cyber threats, ensuring database security and protecting sensitive information have become critical challenges. This study presents an experimental framework to evaluate the performance of various symmetric encryption algorithms—AES, DES, Blowfish, RC4, and ChaCha20—focusing on encryption and decryption efficiency and their impact on Data Manipulation Language (DML) operations, including insertion, selection, and update processes. A payment transaction system was simulated to replicate real-world conditions for assessing DML performance. Performance metrics such as encryption and decryption time were measured under different scenarios involving various database types, encryption key sizes, text lengths, and formats. Custom-developed Python scripts were used to implement the tests, and the results were analyzed using a Microsoft Power BI dashboard for detailed visualization. The findings highlight that RC4 demonstrated the fastest performance across all tested metrics, whereas DES exhibited the longest execution time. AES showed moderate performance compared to the other algorithms. These insights provide valuable guidance for researchers and practitioners on selecting encryption algorithms based on performance requirements, contributing to improved database security and operational efficiency.