Enhancing Medical Image Security through a Novel Framework: Crypto-aware Elliptic Curve Diffie-hellman with Key Derivation Function

To develop and apply advanced methods to enhance medical image security, ensuring patient data integrity, confidentiality, and authenticity throughout the stages of image collection, transmission, and storage.

Retaining patient privacy and data accuracy in the context of accessible healthcare require the secure broadcast and storage space of medical imaging. Because of the increasing dependence on digital medical imaging technology, it is essential to protect these private images from illegal access and possible cyber attacks.

Work addresses the drawbacks of conventional encryption techniques in the healthcare sector and offers a novel Crypto-Aware Elliptic Curve Diffie Hellman with Key Derivation Function (CAECDH-KDF) encryption technique to improve the security of medical images.

The suggested encryption architecture combine domain-specific methods designed for medical imaging data with sophisticated cryptographic algorithms. The framework, in difference to conservative encryption methods, employs an effective tactic that strikes a compromise between processing speed and security. To achieve this, better encryption methods for medical image characteristics are incorporated.

Comparisons are made between the suggested method's security, computation time (0.003001), encryption time (0.001998s), decryption time (0.001001s), entropy (7.997633), and throughput (4.0887) of conventionally encrypted approaches.

A large amount of test images have been utilized to evaluate the effectiveness of the suggested technique. According to numerous tests, the suggested strategy outperforms conventional methods.