基于云计算的蛋白质折叠识别的隐私保护方法
Ali Burak Ünal1,2, Nico Pfeifer3,2, Mete Akgün1,2
1Medical Data Privacy and Privacy Preserving Machine Learning (MDPPML), Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany.
Patterns (New York, N.Y.)
|November 21, 2024
概括
我们开发了一种基于云的安全机器学习服务,用于蛋白质折叠识别,以保护敏感数据. 这种保护隐私的解决方案与现有的模型性能相匹配,并适用于现实世界的使用.
科学领域:
- 计算生物学是一种计算生物学.
- 机器学习是机器学习.
- 密码学 密码学 密码学 密码学
背景情况:
- 基于云的机器学习即服务 (MLaaS) 提供了可访问性,但也引发了隐私问题,特别是在敏感的医疗应用中,如蛋白质折叠识别.
- 保护数据 (蛋白质序列) 和模型对于医疗保健中的安全MLaaS至关重要.
研究的目的:
- 提出一种用于蛋白质折叠识别的新型MLaaS解决方案,通过安全的第三方计算来确保隐私.
- 为复杂的机器学习操作开发高效的私有计算构建块.
主要方法:
- 为MLaaS实施了一个安全的三方计算框架.
- 开发了用于基本操作 (加法,乘法等) 的私人构建块. ) 的情况.
- 使用保护隐私的反复内核网络 (RKN) 演示了这种方法.
主要成果:
- 保护隐私的RKN实现了与非私人模型相比的性能.
- 该解决方案证明了RKN参数的线性可扩展性,表明了实际可行性.
- 开发的私人构建块有效地支持复杂的计算.
结论:
- 拟议的MLaaS解决方案有效地保护了蛋白质折叠识别中的隐私.
- 该框架可适应其他医疗机器学习任务,增强安全的MLaaS采用.
- 该解决方案为保护隐私的计算生物学研究和部署提供了可行的途径.
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