A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images

Abstract

Medical health records hold personal medical information and should only be accessed by authorized medical personnel or concerned patients. The importance of medical health records privacy is increasing as these records are shared in cloud environments. In this paper, we propose an enhanced system for securing patient data (Medical Health Records) embedded in multiple medical images in fragments for secure transmission and public sharing on the cloud or other environments. To protect the patient’s privacy, Medical Records are first encrypted, and then the ciphertext is broken into several fragments based on the number of multimodal medical images of a patient. A key generator randomly selects medical images from the multimodal image data to embed the encrypted patient health record segment using a modified least significant bit embedding process. The proposed technique enables an extra layer of security as even if files fall into the wrong hands and a fragment of the file is decrypted, it will not present any understandable information until all fragments from other medical images are extracted and combined in the correct order. The experiments are performed using multimodal 3255 MRI scans of 21 patients. The robustness of the proposed method was measured using different metrics such as PSNR, MSE, and SSIM. The results show that the proposed system is robust and that image quality is also maintained. To further study the stego image quality, a deep learning-based classification was applied to the images, and the results show that the diagnosis using stego medical images and performance remains unaffected even after embedding the encrypted data.