EHR-protect: A steganographic framework based on data-transformation to protect electronic health records

Abstract

The increasing digitization of healthcare systems and the shift to Electronic Health Records (EHRs) have introduced critical security challenges, including unauthorized access, data breaches, and confidentiality risks. For example, the rapid exchange of sensitive health data between stakeholders highlights the need for secure data-sharing mechanisms. To address these challenges, steganography emerges as a critical solution by embedding sensitive information within other data forms, reducing the likelihood of unauthorized access and ensuring patient confidentiality. This study presents EHR-Protect, an innovative steganographic framework designed to secure EHRs by embedding them within medical images. Unlike general-purpose images, medical images are susceptible to distortions as they serve as diagnostic tools. EHR-Protect uses logarithmic pixel transformation and adaptive techniques such as difference expansion and EHR magnitude reduction to minimize distortions in carrier medical images. The results of EHR-Protect demonstrate its effectiveness in securely embedding EHRs into medical images with minimal distortions. The proposed method achieves a high Peak Signal-to-Noise Ratio (PSNR) of 91.90 dB and a perfect Structural Similarity Index Measure (SSIM) of 1, ensuring image quality is maintained. MSE values across different cover images show minimal increases, even as secret data payloads rise from 10 to 100 kilobits, indicating controlled distortion. The results confirm that EHR-Protect outperforms existing methods, offering a robust solution for securing the EHR data without compromising medical image integrity.