Jan Aiman, Parah Shabir A, Malik Bilal A
Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India.
Department of Electronics and Communication Engineering, Institute of Technology, University of Kashmir Zakoora, Srinagar, India.
Multimed Tools Appl. 2022;81(13):18829-18853. doi: 10.1007/s11042-022-12653-1. Epub 2022 Mar 9.
Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other's output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.
智慧城市旨在通过利用技术进步来提高生活质量。创新的主要领域之一包括数据密集型医疗系统（也称为大数据智能医疗系统）的设计、实施和管理。智能健康系统需要高效且有弹性的安全框架的支持。智能健康系统需要提供的一个重要方面是及时访问高分辨率医学图像，这些图像约占医学数据的80%。这些图像包含有关患者的敏感信息，因此需要完全加以保护。为防止对医学图像的未经授权访问，图像加密过程已成为全世界研究人员的一项紧迫任务。基于混沌的加密为保护敏感数据不被篡改、修改或黑客攻击铺平了道路。在本文中，我们提出了一种基于 Hessenberg 变换和混沌加密的图像加密框架（IEFHAC），用于在加密患者数据时提高安全性并减少计算时间。IEFHAC 使用两个一维混沌映射：逻辑斯谛映射和正弦映射来混淆数据，而扩散则通过应用 Hessenberg 族变换来实现。正弦映射和逻辑斯谛映射用于相互再生地影响对方的输出，从而动态地改变关键参数。实验分析表明，IEFHAC 显示出更好的结果，如 NPCR 范围为 99.66%至 100%，UACI 为 37.39%，计算时间更短，为 0.36 秒，并且对统计攻击更具鲁棒性。
