论文标题
差异隐私对分类公平性的影响有限
Differential Privacy has Bounded Impact on Fairness in Classification
论文作者
论文摘要
从理论上讲，我们研究差异隐私对分类公平性的影响。我们证明，鉴于一类模型，相对于模型的参数，流行的群体公平度量与LIPSCHITZ连续。该结果是对在任意事件（例如敏感组的成员资格）进行准确性的更一般性陈述的结果，这可能具有独立的利益。我们使用这种Lipschitz属性来证明一种非反应约束，表明随着样本的数量的增加，私人模型的公平水平变得更接近其非私人对应物之一。这还强调了模型信心范围对差异隐私的不同影响的重要性。
We theoretically study the impact of differential privacy on fairness in classification. We prove that, given a class of models, popular group fairness measures are pointwise Lipschitz-continuous with respect to the parameters of the model. This result is a consequence of a more general statement on accuracy conditioned on an arbitrary event (such as membership to a sensitive group), which may be of independent interest. We use this Lipschitz property to prove a non-asymptotic bound showing that, as the number of samples increases, the fairness level of private models gets closer to the one of their non-private counterparts. This bound also highlights the importance of the confidence margin of a model on the disparate impact of differential privacy.
