未知 - 意识双边依赖优化用于防御模型倒置攻击
概括
本研究介绍了双边依赖优化 (BiDO) 以保护训练数据隐私免受模型反向攻击. 一个增强的框架,BiDO+,也改善了分布之外的检测,以提高安全性.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 模型倒置 (MI) 攻击通过从分类器中恢复训练数据来威胁数据隐私.
- 单边依赖优化可以缓解MI,但会损害分类性能.
- 这在隐私和模型实用性之间产生了权衡.
研究的目的:
- 开发一种新的战略,双边依赖优化 (BiDO),以加强对MI攻击的隐私,而不牺牲分类性能.
- 为了解决 BiDO 模型在分销之外 (OOD) 检测能力的下降.
- 提出一个升级的框架,BiDO+,整合OOD检测以实现全面的隐私和安全.
主要方法:
- 建议双边依赖优化 (BiDO) 以尽量减少特征隐藏依赖,同时最大限度地提高隐藏标签依赖.
- 在BiDO模型中确定了OOD检测的减少.
- 将辅助OOD数据集成到BiDO中,以创建BiDO+以改进OOD检测.
主要成果:
- BiDO有效地增强了对MI攻击的隐私.
- BiDO模型显示OOD检测性能降低.
- 与BiDO-HSIC相比,BiDO+框架显著改善了OOD检测,减少了FPR95的55.02%并提高了AUCROC的9.52%,具有可比的效用.
结论:
- BiDO提供了一种双重目标的方法,以平衡隐私和分类性能.
- BiDO+成功地解决了与OOD检测限制相关的安全风险.
- 拟议的框架为深度学习系统的隐私和安全提供了强大的解决方案.
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