Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair machine learning aims at minimizing discrimination against protected groups by, for example, imposing a constraint on models to equalize their behavior across different groups. This can subsequently change the influence of training data points on the fair model, in a disproportionate way. We study how this can change the information leakage of the model about its training data. We analyze the privacy risks of group fairness (e.g., equalized odds) through the lens of membership inference attacks: inferring whether a data point is used for training a model. We show that fairness comes at the cost of privacy, and this cost is not distributed equally: the information leakage of fair models increases significantly on the unprivileged subgroups, which are the ones for whom we need fair learning. We show that the more biased the training data is, the higher the privacy cost of achieving fairness for the unprivileged subgroups will be. We provide comprehensive empirical analysis for general machine learning algorithms.

翻译：分析公平和隐私是值得信赖的机器学习的重要支柱。 公平机器学习的目的是通过对不同群体之间行为平等的模式施加限制,从而最大限度地减少对受保护群体的歧视。 这可以以不相称的方式随后改变培训数据点对公平模式的影响。 我们研究这如何改变模式对培训数据的信息泄漏。 我们通过成员推论攻击的镜头分析群体公平(如机会均等)的隐私风险:推断是否使用一个数据点来培训模型。 我们表明公平是以隐私为代价的,而这种成本分配不均:公平模型的信息泄漏在非优先分组中大大增加,我们需要公平学习这些分组。我们表明,培训数据偏差越大,为不优先分组实现公平所需的隐私成本就越高。我们为一般机器学习算法提供了全面的经验分析。