通过模型行为指标对ML预处理进行隐私保护验证.
Wenbiao Li1, Anisa Halimi2, Jaideep Vaidya3
1Case Western Reserve University, Cleveland, OH 44106 USA.
IEEE transactions on privacy
|January 19, 2026
概括
我们开发了一种保护隐私的方法来验证机器学习数据的预处理. 这种方法使用模型行为分析来确保管道完整性,而不需要原始数据或标签.
科学领域:
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
- 模型验证 模型验证
背景情况:
- 确保数据预处理的完整性对于机器学习模型的可靠性至关重要,特别是对于敏感数据.
- 现有的方法通常需要访问原始数据或标签,从而限制它们在保护隐私的场景中的适用性.
研究的目的:
- 引入一个新的隐私保护框架,用于验证数据预处理管道的正确应用.
- 为了使模型验证只使用黑子访问受过训练的模型,没有原始训练数据或基本真相标签.
主要方法:
- 该框架结合了三个行为指标:预测准确度转移,输出分布的Kullback-Leibler (KL) 分歧和解释向量 (LIME/SHAP).
- 它支持二进制正确性决定和缺失预处理步骤的多类诊断.
- 一个没有标签的变体使用解释向量的集群进行验证.
主要成果:
- 二进制探测器甚至在强大的局部差异隐私下 (ε=0.1) 实现了超过75%的F1得分.
- 机器学习分类器在二进制分类任务中表现优于简单的值规则.
- 在分类器和多类诊断门规则之间观察到可比的性能.
结论:
- 拟议的框架提供了一个实用且可扩展的解决方案,用于保护隐私敏感机器学习中的预处理完整性.
- 该方法有效地验证预处理管道,而不影响数据隐私或需要广泛的数据访问.
- 没有标签的变种扩大了验证方法的适用性,以应用于缺乏标签管道示例的场景.
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