从分布式数据与差别私有合成数据进行协作学习
Lukas Prediger1, Joonas Jälkö2,3, Antti Honkela3
1Aalto University, Espoo, 00076, Finland. lukas.m.prediger@aalto.fi.
BMC medical informatics and decision making
|June 14, 2024
概括
分享保护隐私的合成数据使多方能够进行协作学习. 这种方法提高了统计准确性,特别是对于小或代表性不足的数据集,克服了生物医学研究中的隐私障碍.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学研究生物医学研究
- 数据 隐私 数据 隐私 数据
背景情况:
- 合作学习受到隐私问题和无法汇集敏感数据的困扰.
- 没有中央协调的分散计算对联合分析提出了挑战.
- 这项研究探讨了使用保护隐私的合成数据用于英国生物库数据上的协作学习.
研究的目的:
- 评估结合合成数据用于协作学习的可行性.
- 评估数据大小,参与者数量和分布转移对学习成果的影响.
- 确定合成数据共享是否可以克服研究中的隐私和数据访问限制.
主要方法:
- 通过分割英国生物银行队列来模拟多个参与者.
- 为每个模拟方生成差别私密的合成数据.
- 在综合合成数据上应用Poisson回归分析,并与本地数据分析进行比较.
主要成果:
- 与合成数据一起的协作学习产生了比单独使用本地数据更准确的回归参数估计.
- 即使在小的,异质的数据集中也观察到改善.
- 增加各方的参与导致了更大,更一致的改进,直到一个点.
- 综合数据共享特别有利于对代表性不足的群体进行分析.
结论:
- 分享合成数据是保护隐私的协作学习的可行策略.
- 这种方法可以从敏感数据中学习,而不影响隐私,即使局部数据集有限或非代表性.
- 保护隐私的协作学习方法可以缓解生物医学研究中不可访问的分布式敏感数据造成的瓶.
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