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    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.