AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (3.2 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access | Just Accepted

Quantum-Inspired Sensitive Data Measurement and Secure Transmission in 5G-Enabled Healthcare Systems

Xiaohong LvShalli RaniS. ManimuruganAdam SlowikYanhong Feng( )

Abstract

The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures. This study proposes a novel framework for healthcare applications that integrates 5G communication, quantum computing, and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data. The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems, ensuring user anonymity and data confidentiality. The framework is applied to innovative healthcare scenarios, such as secure 5G voice communication, data transmission, and short message services. Experimental results demonstrate the framework’s high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems, surpassing existing approaches.

Tsinghua Science and Technology
Cite this article:
Lv X, Rani S, Manimurugan S, et al. Quantum-Inspired Sensitive Data Measurement and Secure Transmission in 5G-Enabled Healthcare Systems. Tsinghua Science and Technology, 2024, https://doi.org/10.26599/TST.2022.90100

372

Views

105

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics

Available online: 04 July 2024

© The author(s) 2024.

Return