A Hybrid Security Model for the Protection of Diagnostic Text Data in Medical Images over Internet of Things

R. Pavaiyarkarasi, R. Ramu, G. Sahaana, L. Saravanan, R. Begam, R. Prabu
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引用次数: 1

Abstract

As innovation for transmitting data has advanced exponentially, new avenues for protecting sensitive information have emerged up. Over the last several decades, many different strategies, such as steganography and cryptography, have been developed to safeguard sensitive information. As the use of IoT devices in healthcare has grown exponentially, concerns about patient privacy and confidentiality have surfaced as important roadblocks for healthcare service systems. Security in device-to-device communication is a challenging subject. There are a plethora of existing cryptographic techniques for use, such as Data Encryption Standard (DES), Rivest-Shamir-Adleman (RSA), and Advanced Encryption Standard. In this research, we provide hybrid security architecture for safeguarding medical image files that include interpretive text. The proposed scheme uses 2D-DWT to encrypt and conceal sensitive information. Both color and black-and-white photos are used as text covers. The proposed system’s efficacy was evaluated using a battery of tests that included PSNR, SSIM, MSE, and Correlation. The suggested model disguised sensitive patient information in a way that was comparable to traditional methods in terms of throughput, invisibility, and damage to the received steno-image. To implement the suggested system, we turn to MATLAB, with throughput and execution time serving as key metrics for evaluation.
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基于物联网的医学图像诊断文本数据保护混合安全模型
随着数据传输技术的飞速发展,保护敏感信息的新途径也应运而生。在过去的几十年里,许多不同的策略,如隐写术和密码学,已经开发出来保护敏感信息。随着物联网设备在医疗保健领域的使用呈指数级增长,对患者隐私和机密性的担忧已成为医疗保健服务系统的重要障碍。设备对设备通信的安全性是一个具有挑战性的课题。有大量现有的加密技术可供使用,例如数据加密标准(DES)、Rivest-Shamir-Adleman (RSA)和高级加密标准。在这项研究中,我们提供了混合安全架构来保护包含解释文本的医学图像文件。该方案利用2D-DWT对敏感信息进行加密和隐藏。彩色和黑白照片都可用作文字封面。使用包括PSNR、SSIM、MSE和相关性在内的一系列测试来评估拟议系统的有效性。所建议的模型以一种与传统方法在吞吐量、不可见性和对接收的速记图像的损害方面相当的方式掩盖了敏感的患者信息。为了实现建议的系统,我们转向MATLAB,吞吐量和执行时间作为评估的关键指标。
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