可解释的旋转等值多值网络用于属性模糊化
IEEE transactions on pattern analysis and machine intelligence
|August 15, 2025
概括
本研究引入了一个旋转等值神经网络 (RENN),以防止神经网络特征的私有信息泄露. RENN使用多项功能和旋转模糊数据,有效保护输入信息.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 神经网络可以通过中间层特征泄露私人输入信息.
- 攻击者可以逆转这些特征来重建敏感数据.
- 防止泄漏的现有方法可能在计算上昂贵或复杂.
研究的目的:
- 提出一种通用方法来防止神经网络中的信息泄漏.
- 引入一个新的多次估值旋转等值神经网络 (RENN).
- 确保拟议的方法保持神经网络特征对于下游任务的实用性.
主要方法:
- 将实值特征转换为多项特征.
- 在多项功能阶段隐藏输入信息.
- 应用旋转转换 (使用私钥) 进行属性模糊.
- 通过修改经典的神经网络操作,设计RENN以旋转等价.
主要成果:
- 即使网络参数和特征受到损害,RENN也有效地防止信息泄露.
- 加密过程保留了空间相关性,允许与卷积运算无集成.
- 实验结果显示,在防止信息泄露方面有显著改善,分类准确度仅略有下降.
- RENN的计算成本远低于同态加密.
结论:
- 在神经网络中,RENN提供了一种强大而高效的解决方案,以防止信息泄露.
- 旋转等差属性对于维护功能实用程序后加密至关重要.
- 在机器学习模型中,RENN提出了一种实际的方法,可以在不显著地影响性能的情况下增强机器学习模型中的数据隐私.
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