An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2024-09-17 DOI:10.1109/TASE.2024.3456209
Kishu Gupta;Deepika Saxena;Pooja Rani;Jitendra Kumar;Aaisha Makkar;Ashutosh Kumar Singh;Chung-Nan Lee
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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 behavior of real-life users in healthcare processes.
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用于医疗数据管理的智能量子网络安全框架
数字医疗对于方便消费者轻松访问和传播其医疗数据以增强医疗服务至关重要。然而,跨医疗保健系统的数字化需要一个快速、高效和安全的存储设施以及强有力的通信策略,以刺激敏感的数字医疗保健数据共享和对恶意实体的主动估计。在此背景下,本文介绍了一个全面的基于量子的框架,以克服安全医疗保健数据管理的潜在安全和隐私问题。它通过使用量子加密为共享云平台上的医疗保健数据的安全存储和分散配备了量子加密。此外,该框架还提供了一个量子前馈神经网络单元,用于在授予访问权限之前检查数据请求背后的意图,以便主动估计潜在的数据泄露。通过这种方式,提议的框架通过将先进且更有效的量子方法与机器学习相结合,以自动化的方式保护数据存储、访问和恶意实体的预测,从而提供整体医疗保健数据管理。因此,拟议的IQ-HDM导致更加合作和有效的医疗保健服务,并赋予个人充分保管其健康数据的权力。在处理与医疗数据安全相关的网络威胁方面,对所提出的IQ-HDM框架与最先进方法的实验评估和比较概述了高达67.6%的显著改进。从业人员注意事项—本文旨在解决数字医疗保健数据访问问题,这需要既方便又安全。现有的研究要么只关注安全访问,要么关注高安全性,这往往带来很高的计算挑战。在本文中,我们提出了一种综合的方法,考虑到各种挑战,如安全的数据存储,有效的数据通信和恶意实体的预测。我们开发了一个数学系统来描述医疗保健数据的整体管理。本文中提出的所有技术都已使用量子计算实现,并已在四个医疗保健数据集上进行了测试。初步实验结果表明,该方法是可行的。我们的技术可用于发现恶意实体并了解医疗保健流程中真实用户的行为。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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