An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management

Journal article


Gupta, K., Saxena, D., Rani, P., Kumar, J., Makkar, A. and Singh, A. K. 2024. An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management. IEEE Transactions on Automation Science and Engineering. pp. 1-12. https://doi.org/10.1109/TASE.2024.3456209
AuthorsGupta, K., Saxena, D., Rani, P., Kumar, J., Makkar, A. and Singh, A. K.
Abstract

Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioners —This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data communication, and the prediction of malicious entities. We have developed a mathematical system to portray the overall management of healthcare data. All techniques proposed in this paper have been implemented using quantum computing and have been tested on four healthcare datasets. Initial experimental results suggest that the proposed approach is feasible. Our techniques can be applied to discover malicious entities and understand the behaviour of real-life users in healthcare processes.

KeywordsAutomated healthcare data security; malicious entity prediction; quantum encryption; quantum feed forward neural network
Year2024
JournalIEEE Transactions on Automation Science and Engineering
Journal citationpp. 1-12
PublisherIEEE
ISSN1558-3783
Digital Object Identifier (DOI)https://doi.org/10.1109/TASE.2024.3456209
Web address (URL)https://ieeexplore.ieee.org/document/10681488
Accepted author manuscript
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All rights reserved
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Open
Output statusPublished
Publication dates
Online17 Sep 2024
Publication process dates
Accepted2024
Deposited08 Jan 2025
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