通过FHE和zk-SNARK启用边缘聚合,对IoMT进行可扩展的隐私保护数据分析
Soufiane Ben Othman1, Nahom Mihret2
1Applied College, King Faisal University, 31982, Al-Ahsa, Saudi Arabia.
Scientific reports
|January 13, 2026
概括
MedGuard通过对加密的生理数据进行安全的数据聚合和分析来增强医疗物联网 (IoMT) 的安全性. 这种新的框架提高了智能医疗保健系统的隐私和效率.
科学领域:
- 网络安全 网络安全
- 医疗信息学 医疗信息学
- 应用密码学 应用密码学
背景情况:
- 医疗物联网 (IoMT) 促进实时健康监控,但面临着重大隐私和安全风险.
- 现有的框架努力在IoMT数据聚合中平衡数据机密性,可验证性和计算效率.
- 在IoMT中敏感的生理数据聚合容易受到数据泄露,聚合器不当行为和对抗性攻击的影响.
研究的目的:
- 为IoMT提出MedGuard,一个端到端的安全数据聚合框架.
- 为了实现对加密数据的复杂分析查询,确保隐私和监管合规性.
- 通过可验证的计算来消除可信任的中介机构和减轻内部威胁.
主要方法:
- 使用CKKS方案集成完全同型加密 (FHE).
- 用 Groth16 的零知识简要的非交互式知识论证 (zk-SNARKs) 来进行可验证的计算.
- 使用混合数据集的OMNeT++ 6.0.1进行高保真模拟.
主要成果:
- 与基线相比,终端到终端延迟 (64.8毫秒) 实现了13.3%的改善.
- 证明了高通信效率 (1.465 GB/s) 和低能耗 (1.489 mJ 每个查询).
- 包,汇总和查询的持续高吞吐量,确保可扩展性.
结论:
- MedGuard为IoMT数据分析提供了一个可扩展,可验证和保护隐私的解决方案.
- 该框架有效地解决了智能医疗保健中关键的安全和隐私挑战.
- MedGuard支持直接对加密数据进行高级分析,增强临床决策.
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