加强智能医疗网络:集成基于属性的加密以优化和反腐败机制
Yanzhao Zeng1, Xin Guan2, Jingjing Sun3
1School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.
Heliyon
|January 6, 2025
概括
基于属性的加密 (ABE) 通过提高数据安全性和反腐败措施来增强智能医疗保健网络. 与敏感健康数据的其他加密方法相比,ABE提供了卓越的效率,安全性和稳定性.
科学领域:
- 网络安全 网络安全
- 医疗信息学 医疗信息学
- 密码学 密码学 密码学
背景情况:
- 智能医疗保健网络面临着重大数据安全风险和漏洞.
- 确保数据完整性和可靠性对于有效的医疗数据管理至关重要.
- 现有的加密方法可能无法完全解决智能医疗环境的独特挑战.
研究的目的:
- 研究基于属性加密 (ABE) 在智能医疗网络中的可行性和有效性.
- 评估ABE在加强数据完整性反腐败机制方面的作用.
- 将ABE的性能与医疗数据管理中的其他加密算法进行比较.
主要方法:
- 基于属性的加密 (ABE) 与基于身份的加密,数据加密标准,高级加密标准和Rivest-Shamir-Adleman算法的实证比较.
- 评估指标包括访问延迟,数据传输速度,系统稳定性和反腐败能力.
- 对智能医疗保健网络的漏洞分析和反腐败机制的设计.
主要成果:
- 基于属性的加密 (ABE) 显示平均访问延迟为31.6毫秒,数据传输速度为3.56 MB/s.
- ABE实现了98.74%的平均系统稳定性.
- ABE有效地防止数据改和滥用,确保在反腐败框架内安全传输数据.
结论:
- 基于属性的加密 (ABE) 为智能医疗数据管理提供了更有效,更安全,更稳定的解决方案.
- ABE强大的反腐败能力使其成为保护医疗保健数据完整性和安全性的最佳选择.
- ABE集成是一种可行和有效的策略,可以提高智能医疗网络的安全性.
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