集成先进的神经网络架构与隐私增强加密,以实现安全和智能医疗分析
C Ramesh Babu Durai1, S Dhanasekaran2, M Jamuna Rani3
1Kings Engineering College, Chennai, India.
Scientific reports
|August 19, 2025
概括
保护敏感的医疗保健数据至关重要. 一种名为NeuroShield的新方法使用先进的加密和人工智能来保护患者信息,同时确保准确的分析,达到98.73%的准确性.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健数据越来越容易受到泄露和网络攻击的影响.
- 在相互连接的系统中,保护私人患者信息是一个重大问题.
研究的目的:
- 介绍NeuroShield,一种结合CNN-LSTM架构与隐私安全加密的新型结构.
- 在保持数据保护的同时提供安全的医疗保健分析.
主要方法:
- 使用混合CNN-LSTM架构与基于隐私的加密集成.
- 实现基于特征的访问控制 (ABAC),高级加密标准 (AES) 和多因素身份验证 (MFA).
- 整合了差异性隐私优化和可解释AI (XAI) 以提高可解释性.
主要成果:
- 在Kaggle医疗保健数据集上实现了98.73%的高准确性.
- 与医疗保健数据保护和分析方面的现有方法相比,表现出优越的性能.
- 验证了NeuroShield在维护隐私和提供高保真分析方面的有效性.
结论:
- NeuroShield提供了一个强大的解决方案来保护医疗保健数据.
- 该系统增强了数据安全性,并提供了准确,可解释的医疗分析.
- NeuroShield在保护敏感健康信息方面取得了重大进展.
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