一个分散的隐私保护XR系统用于3D医疗数据可视化,使用混合生物识别加密系统
Shreyansh Sharma1,2, Debasis Das3, Santanu Chaudhury4
1Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India. shreyanshsharma665@gmail.com.
Scientific reports
|August 5, 2025
概括
本研究介绍了一种安全的去中心化扩展现实 (XR) 框架,用于使用混合生物识别加密系统 (HBC) 和区块链进行协作外科规划. 它增强了3D医疗数据可视化,并确保了数据完整性和隐私.
科学领域:
- 医学成像和可视化 医学成像和可视化
- 医疗保健中的网络安全
- 扩展现实 (XR) 技术
背景情况:
- 数字医疗需要安全的3D医疗数据可视化,用于手术规划.
- 集中式系统面临安全风险,缺乏对复杂解剖的深度线索.
- 现有的解决方案在隐私,安全和实时协作可视化方面扎.
研究的目的:
- 开发一个分散的基于扩展现实 (XR) 的框架,用于安全和准确的3D医疗数据可视化.
- 整合混合生物识别加密系统 (HBC),区块链和IPFS,以提高安全性和隐私.
- 为了实现直观的,协作性的手术规划,用AR和MR的摄影现实3D模型进行AR和MR.
主要方法:
- 实施了混合生物识别加密系统 (HBC),将同型加密 (HE) 和模糊地 (FV) 结合起来进行身份验证.
- 利用分层编辑区块链进行防改和安全访问控制.
- 开发了一个用于在增强现实 (AR) 和混合现实 (MR) 中染3D脑MRI数据的系统,具有边缘缓存.
主要成果:
- 实现了0.53% (AR) 和0.68% (MR) 的低等错误率 (EER),认证延迟低于530ms.
- 对重播,伪造和未经授权的编辑等安全威胁表现出抵抗力.
- 用户研究证实了临床实用性,可用性和遵守GDPR和HIPAA法规.
结论:
- 拟议的去中心化XR框架为协作医疗数据可视化提供了一个可扩展,安全和沉浸式平台.
- 集成HBC,区块链和IPFS有效地解决了传统集中式系统的局限性.
- 该框架支持实时交互,并增强数字医疗环境中的手术规划.
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