A Dual-Layer Security Framework for Protecting Sensitive Medical Data in Digital Images
Subhabrata Khara, Sabyasachi Pramanik, Debabrata Samanta · 2025
Safeguarding the security and confidentiality of medical data is vital in today’s healthcare landscape. This study introduces a hybrid framework that combines encryption and steganography to protect sensitive medical information effectively. By utilizing the Fernet symmetric encryption algorithm, medical data is securely encrypted before being embedded into digital images using advanced steganography techniques. The approach incorporates Least Significant Bit (LSB) steganography and edge-based data hiding methods, ensuring the encrypted data remains imperceptible while preserving the caliber of medical pictures for diagnostic purposes. The proposed frame-work addresses critical challenges, including maintaining data integrity, confidentiality, and resilience against potential attacks, making it highly suitable for electronic health records (EHRs) and telemedicine applications. Experimental results highlight the framework’s ability to achieve robust security, high embedding capacity, and minimal distortion of the host image. This dual-layer security approach not only protects patient data but also aligns with stringent regulatory requirements for medical data protection, ensuring a reliable and secure healthcare ecosystem.