A novel medical image data protection scheme for smart healthcare system

Rehman, M. U., Shafique, A., Khan, M. S., Driss, M., Boulila, W., Ghadi, Y. Y., Changalasetty, S. B., Alhaisoni, M. and Ahmad, J. (2024) A novel medical image data protection scheme for smart healthcare system. CAAI Transactions on Intelligence Technology, (doi: 10.1049/cit2.12292) (Early Online Publication)

Abstract

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge-based learning systems. Smart healthcare systems leverage knowledge-based learning to become more context-aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X-rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge-based learning systems to foster structured decision-making and enhance the learning abilities of AI. Moreover, in knowledge-driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit-plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high-quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.

• Item Type: Articles
• Additional Information: The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a large group Research Project under grant number RGP.2/379/44.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shafique, Dr Arslan
• Authors: Rehman, M. U., Shafique, A., Khan, M. S., Driss, M., Boulila, W., Ghadi, Y. Y., Changalasetty, S. B., Alhaisoni, M., and Ahmad, J.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: CAAI Transactions on Intelligence Technology
• Publisher: Wiley
• ISSN: 2468-2322
• ISSN (Online): 2468-2322
• Published Online: 13 February 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in CAAI Transactions on Intelligence Technology 2024
• Publisher Policy: Reproduced under a Creative Commons license