用于生成合成数据的联合学习:一个范围审查
Claire Little1, Mark Elliot2, Richard Allmendinger3
1Cathie Marsh Institute for Social Research, School of Social Sciences, University of Manchester, Oxford Road, M13 9PL, Manchester, UK.
International journal of population data science
|February 28, 2024
概括
联合学习 (FL) 允许在不共享本地数据的情况下生成合成数据,提供了一种有希望的保护隐私的方法. 需要进一步的研究来探索相关的隐私风险和评估方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 联邦学习 (FL) 在分散的客户端中训练模型,通过将数据保持在本地环境中来增强隐私.
- 合成数据生成创建模仿原始数据属性的人工数据集,最大限度地降低披露风险.
- 联合合成将FL和合成数据生成结合起来,在没有原始数据共享的情况下创建全球数据集.
研究的目的:
- 审查目前使用FL用于合成数据生成的研究和实践.
- 在联合合成中识别方法,评估实践和研究缺口.
主要方法:
- 一个范围审查系统地绘制和描述了FL的文献,用于合成数据生成.
- 包括在2018年至2023年间发表的69篇文章.
- 提取的信息涵盖了数据类型,模型架构,以及对实用性和隐私的评估.
主要成果:
- 30%的研究集中在合成数据生成,其中6个产生表格数据.
- 59%的研究集中在数据增强上.
- 执行联合合成的所有21项研究都使用了深度学习,主要是生成对抗网络.
结论:
- 联合合成是一种新兴的方法,用于创建全球合成数据集,同时保持客户端数据隐私.
- 该领域需要进一步探索与不同方法相关的隐私风险.
- 需要标准化方法来衡量联邦合成中的隐私风险.
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