以安全,包容和有弹性的方式利用共享数据的潜力,通过多键同型加密
David Ha Eun Kang1, Duhyeong Kim2, Yongsoo Song3
1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA. haeun@mit.edu.
Scientific reports
|June 13, 2024
概括
本研究介绍了一种安全的多键同型加密逻辑回归 (MK-HELR) 算法,用于协作机器学习. 它使多个数据贡献者能够从组合,加密的数据中获得洞察力,同时保持隐私并允许动态的参与者添加.
科学领域:
- 计算机科学 计算机科学
- 统计学学习 统计学学习
- 密码学 密码学 密码学 密码学
背景情况:
- 机器学习模型从聚合数据中获益,以改善概括.
- 现有的多方学习框架往往缺乏参与者数量的灵活性.
- 数据隐私是数据贡献者在协作设置中的关键问题.
研究的目的:
- 开发一个安全的,多方框架,用于机器学习的综合数据源.
- 引入一种算法,使加密的多方数据能够进行后勤回归.
- 促进具有灵活成员制的动态协作学习环境.
主要方法:
- 开发多键同型加密逻辑回归算法 (MK-HELR).
- 在物流回归过程中应用同态加密来保护多方数据.
- 实施一个动态的会员模式,用于参与者的纳入/排除.
主要成果:
- MK-HELR算法在加密的多方数据上成功执行了后勤回归.
- 该框架确保了所有贡献者的数据隐私.
- 动态会员功能允许在学习过程中无地整合新参与者.
结论:
- 该MK-HELR算法为协作机器学习提供了安全和灵活的解决方案.
- 这种方法提高了数据的实用性,同时保持了个人数据的隐私.
- 该框架支持动态参与者场景,对于现实世界的应用至关重要.
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