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增强智能医疗网络：集成基于属性的加密以实现优化和反腐败机制。

Enhancing smart healthcare networks: Integrating attribute-based encryption for optimization and anti-corruption mechanisms.

作者信息

Zeng Yanzhao, Guan Xin, Sun Jingjing, Chen Yanrui, Wang Zeyu, Nie Peng

机构信息

School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.

Guangzhou Xinhua University, Dongguan, 523133, China.

出版信息

Heliyon. 2024 Oct 16;11(1):e39462. doi: 10.1016/j.heliyon.2024.e39462. eCollection 2025 Jan 15.

DOI:10.1016/j.heliyon.2024.e39462
PMID:39758381
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11699327/
Abstract

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 ms, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74 %. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

摘要

本研究调查了将基于属性的加密（ABE）集成到智能医疗网络中的可行性和有效性，特别关注其在增强反腐败机制方面的作用。该研究对这些网络当前的漏洞进行了全面分析，识别潜在的数据安全风险。设计了一种反腐败机制以确保数据的完整性和可靠性。然后将ABE方法与其他著名的加密算法进行实证比较，如基于身份的加密、数据加密标准、高级加密标准和Rivest-Shamir-Adleman算法。这些方法基于访问延迟、数据传输速度、系统稳定性和反腐败能力进行评估。实验结果突出了ABE算法的优势，显示平均访问延迟为31.6毫秒，数据传输速度为3.56MB/s，平均系统稳定性为98.74%。此外，当集成到反腐败机制中时，ABE能有效防止数据篡改和滥用，确保数据传输安全。与替代算法相比，ABE凭借其强大的反腐败能力，为智能医疗网络中的数据管理提供了更高效、安全和稳定的解决方案。这使ABE成为保障医疗数据完整性和安全性的最佳选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/bcabb5e4e81c/gr10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/bcabb5e4e81c/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/957f41c643ff/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/598c26e3d1a5/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/574064119347/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/b0b6929077f2/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/4c5f993cc18f/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/df74d447bc6b/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/88ff8117138f/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/164226977a73/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/62dabc563e51/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/372a/11699327/bcabb5e4e81c/gr10.jpg

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