论文标题
隐私和公平的差异方法
A Variational Approach to Privacy and Fairness
论文作者
论文摘要
在本文中，我们提出了一种新的变分方法来学习私人和/或公平表示。这种方法基于我们提出的隐私和公平优化问题的新提出的拉格朗日人。在此公式中，我们旨在生成数据的表示，以保留私人或敏感数据未共享的相关信息的规定级别，同时最大程度地减少其剩余信息。提出的方法（i）表现出隐私和公平问题的相似之处，（ii）使我们能够通过Lagrange乘数参数控制实用性与隐私或公平性之间的权衡，并且（III）可以舒适地纳入通用代表性学习算法，例如Vae，例如Vae，Vae，Vae，vae，$β$ -VAE，VIB，VIB，vib，vib或nonlinear ib ib ib ib ib ib ib ib ib ib ib ib ib ib ib ib ib ib ib。
In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the $β$-VAE, the VIB, or the nonlinear IB.
