装甲:保护无法学习的例子免受数据增强的影响
IEEE transactions on pattern analysis and machine intelligence
|January 12, 2026
概括
数据增强可以破坏由无法学习的例子保护的私人数据,使深度神经网络 (DNN) 能够学习敏感信息. ARMOR框架有效地防范这些隐私侵犯,确保数据即使在增强后仍然无法学习.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 在线私人数据容易受到未经授权的收集,用于训练深度神经网络 (DNN).
- 无法学习的例子旨在通过尽量减少DNN训练损失来保护数据,使数据难以学习.
- 数据增强是一种常见的预处理步骤,可以无意中恢复受保护数据中的隐私.
研究的目的:
- 调查和揭示数据增强所带来的隐私侵犯风险,使用无法学习的例子.
- 提出一种新的防御框架,ARMOR,以防止数据增强引起的隐私侵犯.
- 在没有直接访问模型培训过程的情况下,开发保护数据隐私的方法.
主要方法:
- 一个非本地模块辅助的替代模型被设计用于模拟数据增强效应.
- 开发了一个代孕增强选择策略,以优化每个类别的增强.
- 动态步骤大小调整算法用于产生防御噪声.
- 在4个数据集和5种增强方法上进行了广泛的实验.
主要成果:
- 数据增强显著提高了模型准确度,无法学习的例子从21.3%增加到66.1%.
- 武器成功地保留了受保护的私人数据对数据增强的不可学习性.
- 与基线方法相比,ARMOR可将测试准确度降低高达60%.
- 拟议的防御框架证明了对抗对手训练的稳定性.
结论:
- 当应用于无法学习的数据时,数据增强对隐私构成重大威胁.
- 亚马尔框架为这些隐私漏洞提供了有效的防御机制.
- 在机器学习管道中,ARMOR提供了一个强大的解决方案来保护私人数据,即使使用复杂的预处理技术.
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