成员盾:一个联合学习的框架,与会员的隐私
Faisal Ahmed1, David Sánchez2, Zouhair Haddi3
1NVISION Systems and Technologies SL, Gran Via Carles III, 124, ent. 1a, 08034, Barcelona, Catalonia, Spain; Universitat Rovira i Virgili, Dept. of Computer Engineering and Mathematics, CYBERCAT-Center for Cybersecurity Research of Catalonia, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain.
概括
成员盾通过减轻会员推断攻击 (MIA) 来提高联邦学习 (FL) 的隐私. 这种方法可以保护敏感的训练数据,而不影响模型性能或增加训练时间.
科学领域:
- 机器学习 机器学习
- 网络安全 网络安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 能够在保护数据隐私的同时实现协作模式培训.
- FL系统容易受到会员推断攻击 (MIA) 的攻击,威胁数据保密性.
- 当前的MIA防御通常会降低模型效用,增加计算开销.
研究的目的:
- 分析FLMIA漏洞的根本原因,源于模型过拟合.
- 提出MemberShield,这是一个新的防御机制,用于对抗FL的MIA.
- 评估MemberShield在隐私保护,模型实用性和效率方面的有效性.
主要方法:
- 成员盾采用双重方法:标签预处理 (一热到软标签) 和基于验证准确性的早期停止.
- 该方法侧重于减少成员和非成员数据样本之间的模型行为差异.
- 经验评估使用三个数据集和四个不同的模型架构进行.
主要成果:
- 成员盾显著超过现有的最先进的防御对所有形式的MIA.
- 拟议的方法实现了卓越的隐私保护,同时保持了高模型实用性.
- "会员盾"显著减少了培训时间,并且易于实施.
结论:
- "会员盾"提供了一种实用且有效的解决方案,用于加强联邦学习中的隐私.
- 基于泛化的防御成功地解决了MIA漏洞,而不影响模型性能.
- 这种方法为安全的协作机器学习提供了可扩展和高效的方法.
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