EPR-Stego: Quality-Preserving Steganographic Framework for Securing Electronic Patient Records

Wardatul Amalia Safitri; Hammuda Arsyad; Ntivuguruzwa Jean De La Croix; Tohari Ahmad; Jennifer Batamuliza; Ahmad Hoirul  Basori

doi:10.35882/jeeemi.v8i1.1172

Wardatul Amalia Safitri Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0009-0001-4009-5257
Hammuda Arsyad Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0009-0006-4791-3159
Ntivuguruzwa Jean De La Croix Department of Business Information Technology, College of Business and Economics, University of Rwanda, Kigali, Rwanda https://orcid.org/0000-0001-5249-1241
Tohari Ahmad Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0000-0002-3390-0756
Jennifer Batamuliza African Center of Excellence in Data Science, College of Business and Economics, University of Rwanda, Kigali, Rwanda https://orcid.org/0000-0001-5757-3852
Ahmad Hoirul Basori Department of Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia https://orcid.org/0000-0001-9684-490X

DOI: https://doi.org/10.35882/jeeemi.v8i1.1172

Keywords: Cyber security, Data hiding, Information security, Medical data privacy, Information hiding

Abstract

Secure medical data transmission is a fundamental requirement in telemedicine, where information is often exchanged over public networks. Protecting patient confidentiality and ensuring data integrity are crucial, particularly when sensitive medical records are involved. Steganography, an information hiding technique, offers a promising solution by embedding confidential data within medical images. This approach not only safeguards privacy but also supports authentication processes, ensuring that patient information remains secure during transmission. This study introduces EPR-Stego, a novel steganographic framework designed specifically for embedding electronic patient record (EPR) data in medical images. The key innovation of EPR-Stego lies in its mathematical strategy to minimize pixel intensity differences between neighboring pixels. By reducing usable pixel variations, the framework generates a stego image that is visually indistinguishable from the original, thereby enhancing imperceptibility while preserving diagnostic quality. Additionally, the method produces a key table, required by the recipient to accurately extract the embedded data, which further strengthens security against unauthorized access. The design of EPR-Stego aims to prevent attackers from easily detecting the presence of hidden medical information, mitigating the risk of targeted breaches. Experimental evaluations demonstrate its effectiveness, with the proposed approach achieving Peak Signal to Noise Ratio (PSNR) values between 51.71 dB and 75.59 dB, and Structural Similarity Index Measure (SSIM) scores reaching up to 0.99. These metrics confirm that the stego images maintain high visual fidelity and diagnostic reliability. Overall, EPR-Stego outperforms several existing techniques, offering a robust and secure solution for medical data transmission. By combining imperceptibility, security, and quality preservation, the framework addresses the pressing need for reliable protection of patient information in telemedicine environments

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