MedRec-Secure: A Framework for Confidentiality Preservation in Medical Patient Records

The digitization of healthcare has revolutionized patient data management through Medical Patient Records (MPRs), but has simultaneously introduced critical security vulnerabilities, as traditional cryptographic methods explicitly reveal the presence of sensitive information. This study introduces MedRec-Secure, a comprehensive data hiding scheme designed to enhance MPR confidentiality through a novel Dynamic Subtractor Selection Steganographic Framework (DSSSF) that conceals sensitive medical information within medical images while preserving diagnostic quality. The proposed framework operates on grayscale medical images divided into 4-pixel blocks, employing statistical analysis to select optimal reference pixels dynamically. A multi-zone embedding strategy categorizes pixels into three intensity zones, with tailored embedding rules for each zone. Experimental evaluation demonstrated superior performance, with a Peak Signal-to-Noise Ratio (PSNR) achieving a maximum of 75.43 dB across varying MPR payload sizes (1 kb to 100 kb). The Structural Similarity Index Measure (SSIM) achieved outstanding maximum values of 1.000, maintaining near-perfect similarity despite slight decreases with larger payloads. MedRec-Secure outperforms existing methods by 3.2 dB in PSNR performance, preserving diagnostic image integrity while enabling secure MPR transmission across healthcare networks.