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All Journal JOIV : International Journal on Informatics Visualization
Said, Lamjed Ben
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Computer Science & IT

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Jaccard-based Random Distribution with Least and Most Significant Bit Hiding Methods for Highly Patients MRI Protected Privacy Tayh Albderi, Ali Jaber; Al-Shammary, Dhiah; Said, Lamjed Ben
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2385

Abstract

In this study, the main goal is to improve patient care by making it easier for patient data and pictures to be sent between medical centers without problems. Still, one of the biggest problems with telemedicine is keeping patient information private and ensuring data is safe. This is especially important because even small changes to patient information could have serious consequences, such as wrong evaluations and lower-quality care. This study develops a new model that uses the unique Jaccard distribution of the least significant bit (LSB) and the most significant bit (MSB) to solve this complex problem. The goal of this model is to hide much information about a patient in the background of an MRI cover picture. The careful creation of this model is a crucial part of the current study, as it will ensure a solid way to hide information securely. A more advanced method is also suggested, which involves randomly putting private text in different places on the cover picture. This plan is meant to strengthen security steps and keep private patient information secret. The peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the mean square error (MSE) all improved significantly when this method was tested in the real world. With these convincing results, the study shows telemedicine is more effective than traditional methods for keeping patient data safe. This proves that the model and method shown have the potential to greatly improve patient privacy and data accuracy in telemedicine systems, which would improve the general quality of health care.
Co-Authors Al-Shammary, Dhiah Tayh Albderi, Ali Jaber