Uldp-FL:联合学习与跨线索用户级别差异性隐私
Fumiyuki Kato1, Li Xiong2, Shun Takagi1
1Kyoto University.
概括
本研究介绍了Uldp-FL,这是一个新的差别私有联合学习 (DP-FL) 框架,可以保证用户级别的隐私. 它解决了一个用户的场景.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 密码学 密码学 密码学 密码学
背景情况:
- 不同的私有联合学习 (DP-FL) 对于具有正式隐私保证的协作机器学习至关重要.
- 现有的DP-FL方法通常可以确保跨 silo FL 的仓库内创纪录的隐私.
- 当单个用户的数据跨越多个孤岛时,实现用户级DP的挑战是一个开放的研究问题.
研究的目的:
- 提出Uldp-FL,一个新的联合学习 (FL) 框架,在跨设置中提供用户级差异性隐私 (DP).
- 为了解决个别用户数据分布在多个数据孤岛的场景.
- 通过分布式用户数据在协作机器学习中建立一个新的隐私标准.
主要方法:
- 开发了Uldp-FL,这是一个框架,通过每个用户的加权剪切来确保用户级DP,与群组隐私方法不同.
- 对算法的隐私和实用性权衡进行了理论分析.
- 实施了基于用户记录分布的增强权重策略,以提高实用性.
- 设计了一个新的私有协议,以防止信息泄露到孤岛和服务器.
主要成果:
- 与基线方法相比,在用户级DP下,在隐私-实用性权衡方面取得了实质性的改进.
- 在现实数据集上的实验结果验证了Uldp-FL框架的有效性.
- 这种新的私有协议成功地阻止了其他信息的披露.
结论:
- Uldp-FL是第一个 FL 框架,在一般的跨 silo FL 环境中有效提供用户级 DP.
- 拟议的方法在平衡分布式机器学习的隐私和实用性方面取得了重大进展.
- 这项工作为分布式用户数据的联合学习系统中的隐私保证设定了新的基准.
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