论文标题
关于算法公平的隐私风险
On the Privacy Risks of Algorithmic Fairness
论文作者
论文摘要
算法公平和隐私是值得信赖的机器学习的基本支柱。公平的机器学习旨在最大程度地限制对受保护群体的歧视，例如，对模型施加限制以使其在不同群体之间均衡其行为。随后可以以不成比例的方式改变培训数据点对公平模型的影响。我们研究如何改变模型有关其培训数据的信息泄漏。我们通过成员推理攻击的镜头分析了团体公平的隐私风险（例如，均衡的几率）：推断数据点是否用于培训模型。我们表明，公平性是以隐私为代价的，而且这一成本并非平等分配：公平模型的信息泄漏在无特权的亚组上大大增加，这是我们需要公平学习的信息。我们表明，培训数据的偏见越多，实现非特权子组公平性的隐私成本就越高。我们为通用机器学习算法提供全面的经验分析。
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.
