An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management
<|reference_start|>An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management: 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.<|reference_end|>
@article{gupta2024an,
    title={An Intelligent Quantum Cyber-Security Framework for Healthcare Data
  Management},
    author={Kishu Gupta, Deepika Saxena, Pooja Rani, Jitendra Kumar, Aaisha
  Makkar, Ashutosh Kumar Singh, Chung-Nan Lee},
    journal={IEEE Transactions on Automation Science and Engineering (2024)},
    year={2024},
    doi={10.1109/TASE.2024.3456209},
    archivePrefix={arXiv},
    eprint={2410.03217},
    primaryClass={cs.CR}
}