Developing an Efficient Image Encryption Algorithm for Secure Data Transmission in Healthcare Systems
As the use of Healthcare Information Technology has continued to rise, the issue of safe transfer of medical information with especial emphasis on images has become a major concern. The findings of this study propose a new approach to the encryption of images that is well suited to applications in healthcare and which guarantees the safety of medical images over weak networks. This proposed algorithm successfully implements a chaotic encryption method alongside wavelet transformation so as to design an effective and virtually invulnerable encryption method that does not consume an excessively large amount of computing power. There is also a way for the algorithm to be tested on X-Ray, CT, and MRI images so that the algorithm is not confined to one particular imaging modality. Such results clearly prove that the proposed algorithm provide better performance than AES as well as RSA with respect to encryption speed and sensitivity of the keys. Image quality after decryption is also maintained making it possible to retain important information about the patient’s health. Due to the algorithms high security, efficiency and capability of processing data in real time healthcare environment the proposed algorithm, provides a feasible solution for securing sensitive healthcare information and is a worthy addition to the literature on healthcare data security.
Authors
- Krishna Kant Dixit
- S Govinda Rao (ORCID: https://orcid.org/0009-0004-3033-4578)
- Hassan Ali (ORCID: https://orcid.org/0000-0002-1701-0390)
- P. Vijaya Vardhan Reddy
- G. Santhi (ORCID: https://orcid.org/0000-0002-7665-5463)
- Srinivasa Reddy Kurukuntla
Institutions
- Iraqi University (IQ)
- Gokula Krishna College of Pharmacy (IN)
- GLA University (IN)
Publication Details
- Published
- 2024-11-23
- DOI
- https://doi.org/10.1109/icicat62666.2024.10923224
- Citations
- 1
- Primary Topic
- Smart Systems and Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.35