基于尖端学习的隐私保护 医疗物联网在Metaverse中的隐私保护
IEEE journal of biomedical and health informatics
|August 21, 2023
概括
我们开发了一个基于生成对抗网络和尖端学习的卷积神经网络 (GASCNN),以保护元宇宙中的医疗图像. 这种方法增强了对数据和类泄漏攻击的安全性,同时提高了能源效率.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
- 网络安全 网络安全
背景情况:
- 包含增强现实和虚拟现实的Metaverse对远程医疗和电子健康越来越重要.
- 在Metaverse应用程序中的医疗数据面临着来自数据和类泄露攻击的重大风险.
- 目前的安全方法,如加密,可以降低医疗服务的性能.
研究的目的:
- 提出一种新的方法,即基于生成对抗网络和尖端学习的卷积神经网络 (GASCNN),用于在Metaverse应用中保护医疗图像.
- 增强对数据和类泄漏攻击的弹性,而不会影响性能.
- 为了提高医疗成像安全的能源效率.
主要方法:
- 利用生成对抗网络 (GANs) 来从残余网络特征地图中创建合成医疗图像.
- 使用转换范式将ResNet模型转换为尖端神经网络 (SNN).
- 采用尖峰学习技术,通过将空间数据映射到时间轴上来加密模型重量,从而阻碍重建.
主要成果:
- GASCNN模型证明了对各种数据和类泄漏攻击的弹性,优于现有方法 (FID分数增加1.75倍).
- 与标准ResNet模型相比,实现了轻微的性能下降 (低于3%).
- 与标准ResNet架构相比,报告了52倍的显著能源效率提升.
结论:
- 拟议的GASCNN提供了一个强大的解决方案,用于在Metaverse环境中保护医疗图像免受复杂的攻击.
- 该方法平衡了安全需求与可接受的性能权衡和大幅节省能源.
- GASCNN为医疗Metaverse应用程序的安全和高效实现提供了一种有前途的方法.
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