迈向实用和保护隐私的CNN推断服务,用于基于云的医疗成像分析:基于同型加密的方法
Yanan Bai1, Hongbo Zhao1, Xiaoyu Shi2
1School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135, China.
Computer methods and programs in biomedicine
|January 28, 2025
概括
本研究介绍了PPCNN,这是一个基于云计算的医疗成像分析隐私保护框架. PPCNN增强了安全性,并减少了医疗保健深度学习应用程序的延迟,使人工智能驱动的解决方案变得更加实用.
科学领域:
- 生物医学数据分析
- 医疗保健中的人工智能
- 云计算安全 云计算安全
背景情况:
- 基于云的深度学习即服务 (DLaaS) 提供了强大的生物医学数据分析功能.
- 向云服务器传输敏感用户数据引发了严重的隐私问题.
- 现有的隐私保护解决方案由于复杂的计算和通信成本而遭受高延迟.
研究的目的:
- 为解决基于移动云计算的医疗成像分析中的隐私和延迟挑战.
- 在云服务器上使用卷积神经网络 (CNN) 实现私人放射图像的安全分类.
- 为CNN推断开发一个实用和保护隐私的框架.
主要方法:
- 提议PPCNN,一个新的框架,结合同态加密和基于噪声的掩盖.
- 通过将昂贵的操作转移到离线阶段来优化计算.
- 实施系数意识包装,以实现高效的线性层操作,并对非线性操作进行数据掩盖,以降低通信成本.
主要成果:
- PPCNN是在三个现实世界放射性图像数据集上实施和评估的.
- 实验结果表明,在移动云场景中,与最先进的方法相比,其性能优越.
- 实现了显著更好的响应时间和更低的使用成本.
结论:
- 推出了基于云计算的医疗成像分析的高效和保护隐私的框架.
- PPCNN代表了朝着实用,安全和值得信赖的人工智能驱动的医疗保健的重大进步.
- 该框架有助于在敏感的医疗应用中采用深度学习.
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