Abstract
In an era of growing cybersecurity threats, as evidenced by the growing reliance on medical data, it is necessary to develop strong encryption algorithms to protect sensitive data against intruders. Though existing encryption algorithms offer some security, they often fail to address the unique challenges posed by safeguarding multimedia medical data against sophisticated attacks. To enhance security, the proposed system employs a distinctive encryption algorithm that integrates a color-based approach with mathematical transformations. It begins with a randomly generated color grid to map plain text characters to hexadecimal values, which are then converted to ASCII and processed bit wise. The algorithm strengthens security further through block-level encryption, utilizing multiple rounds of encryption logic, including XOR operations and circular shifts. Experimental validation demonstrates the method’s effectiveness, with average encryption times of 0.18 s for text data, 111.03 s for image data, and 1520.47 s for video data. The system achieves a decryption error rate of 2–5%, underscoring its high reliability in reconstructing the original data. Furthermore, the proposed algorithm demonstrates resistance against known plaintext, chosen ciphertext, brute force, and differential cryptanalysis attacks, ensuring robust protection of sensitive medical data. These results position the proposed method as a robust and promising solution for addressing encryption challenges in securing sensitive medical data. Currently, the system uses a randomly generated 10×10 fixed-length secret key; however, future work will focus on implementing a variable-length randomly generated key for increased flexibility and security.
Author supplied keywords
Cite
CITATION STYLE
Ch, R., Shaik, J., Srikavya, R., Sahu, M., & Sahu, A. K. (2025). A novel fiestal structured chromatic series-based data security approach. Discover Internet of Things, 5(1). https://doi.org/10.1007/s43926-025-00162-0
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.