使用同态加密的隐私保护深度学习模型的开发:脏CT成像技术可行性研究
Sang-Wook Lee1, Jongmin Choi2, Min-Je Park2
1Department of Anesthesiology and Pain Medicine, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Radiology. Artificial intelligence
|August 27, 2025
概括
这项研究表明,使用同型加密与深度学习进行CT质分析是技术上可行的. 它通过端到端加密实现了可比的诊断准确性,同时确保了数据隐私.
科学领域:
- 医学成像
- 人工智能
- 密码学
背景情况:
- 深度学习模型对于分析质的CT图像是有效的.
- 然而,隐私问题限制了这些模型中敏感患者数据的使用.
- 同型加密为保护隐私的分析提供了潜在的解决方案.
研究的目的:
- 评估将同型加密集成到深度学习中的技术可行性,以保护CT质分析的隐私.
- 评估加密对诊断性能和计算需求的影响.
主要方法:
- 开发了一个有三个阶段的深度学习系统:参考CNN (Ref-CNN),修改为加密 (Approx-CNN) 和完全同型加密 (HE-CNN) 使用CKKS方案.
- 在12446张瘤CT图像上训练和评估模型.
- 使用接收器操作特征曲线下的面积 (AUC) 和精度回忆曲线下的面积 (AUPRC) 评估性能.
主要成果:
- 所有模型的诊断准确度都很高 (AUC: 0. 89- 0. 99, AUPRC: 0. 67- 0. 99).
- 在Ref-CNN和Approx-CNN之间观察到的最小性能权衡.
- 同型加密显著增加了存储 (65KB到32MB) 和计算时间 (50分钟的CPU和90秒的GPU).
结论:
- 使用同态加密的隐私保护深度学习推断在CT图像上进行质分类是技术上可行的.
- 通过端到端的数据加密可以实现类似的诊断性能.
- 计算和存储需求的显著增加需要优化硬件加速.
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