论文标题
黄金并不总是闪闪发光的：光谱删除线性和非线性守卫属性信息
Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information
论文作者
论文摘要
我们描述了一种简单有效的方法（频谱属性去除； SAL），以从神经表示中删除私人或保护信息。我们的方法使用矩阵分解将输入表示形式投射到与受保护信息的协方差降低的方向上，而不是最大值协方差，因为通常使用的分解方法。我们从删除线性信息开始，然后继续将算法概括为使用内核去除非线性信息的情况。我们的实验表明，与以前的工作相比，我们的算法在删除了受保护的信息后保留了更好的主要任务性能。此外，我们的实验表明，我们需要相对少量的保护属性数据来删除有关这些属性的信息，这降低了对敏感数据的接触，并且更适合低资源场景。代码可从https://github.com/jasonshaoshun/sal获得。
We describe a simple and effective method (Spectral Attribute removaL; SAL) to remove private or guarded information from neural representations. Our method uses matrix decomposition to project the input representations into directions with reduced covariance with the guarded information rather than maximal covariance as factorization methods normally use. We begin with linear information removal and proceed to generalize our algorithm to the case of nonlinear information removal using kernels. Our experiments demonstrate that our algorithm retains better main task performance after removing the guarded information compared to previous work. In addition, our experiments demonstrate that we need a relatively small amount of guarded attribute data to remove information about these attributes, which lowers the exposure to sensitive data and is more suitable for low-resource scenarios. Code is available at https://github.com/jasonshaoshun/SAL.
