基于云的安全和耐故障框架,用于在分布式环境中进行医疗图像存储和检索
Arun Amaithi Rajan1, Vetriselvi V2, Ajitesh M2
1Security Research Lab, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, 600025, India. 22144191119@student.annauniv.edu.
这项研究介绍了SFMedIR,这是一个安全的医疗图像检索框架. 它通过使用联合学习和先进加密来提高对抗攻击的安全性和准确性,改善医疗数据管理.
科学领域:
- 医疗成像医学成像
- 云计算 云计算 云计算
- 网络安全 网络安全
背景情况:
- 基于云的集中式医疗图像检索系统面临着重大的安全和可用性挑战.
- 现有的深度学习解决方案很容易受到对抗性攻击和新出现的量子威胁的影响.
- 在医疗图像管理中,急需安全的分布式解决方案.
研究的目的:
- 提出SFMedIR,一个安全和耐故障的医疗图像检索框架.
- 提高基于云的医疗图像系统对抗对抗和量子威胁的弹性.
- 为了提高医学图像检索的准确性和通用性.
主要方法:
- 开发了使用ConvNeXt模型生成哈希代码的抗对抗性攻击的联合学习方法.
- 集成基于量子混沌的加密,提供强大的数据安全性.
- 实现了基于值的动态影子存储和分布式云架构,以实现故障耐受性和减轻单点故障.
主要成果:
- 在脑MRI和脏CT数据集上的对抗查询的检索准确度提高了60-70%.
- 显示总体检索准确率为90%,比现有模型高出5-10%.
- 与传统方法相比,在安全性和检索效率方面都表现出卓越的性能.
结论:
- SFMedIR为基于云的安全医疗图像检索提供了弹性和高效的解决方案.
- 该框架显著提高了医疗保健应用程序的安全性和可用性.
- 这种方法代表了安全医疗图像管理的宝贵进步.
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