This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP) guarantee to protect training data. The key idea of FairDP is to train models for distinct individual groups independently, add noise to each group's gradient for data privacy protection, and progressively integrate knowledge from group models to formulate a comprehensive model that balances privacy, utility, and fairness in downstream tasks. By doing so, FairDP ensures equal contribution from each group while gaining control over the amount of DP-preserving noise added to each group's contribution. To provide fairness certification, FairDP leverages the DP-preserving noise to statistically quantify and bound fairness metrics. An extensive theoretical and empirical analysis using benchmark datasets validates the efficacy of FairDP and improved trade-offs between model utility, privacy, and fairness compared with existing methods. Our empirical results indicate that FairDP can improve fairness metrics by more than 65% on average while attaining marginal utility drop (less than 4% on average) under a rigorous DP-preservation across benchmark datasets compared with existing baselines.

翻译：本文提出FairDP，一种新颖的训练机制，旨在为训练后模型的决策提供群体公平性认证，同时通过差分隐私（DP）保证来保护训练数据。FairDP的核心思想是：为不同个体群体独立训练模型，为每个群体的梯度添加噪声以实现数据隐私保护，并逐步整合来自各群体模型的知识，从而构建一个在下游任务中平衡隐私性、实用性与公平性的综合模型。通过这种方式，FairDP确保每个群体贡献均等，同时能控制添加到各群体贡献中的DP保护噪声量。为提供公平性认证，FairDP利用DP保护噪声对公平性指标进行统计量化与边界限定。基于基准数据集的大量理论与实证分析验证了FairDP的有效性，并证明其相较于现有方法在模型实用性、隐私性与公平性之间实现了更优的权衡。实证结果表明，在基准数据集上采用严格的DP保护前提下，与现有基线方法相比，FairDP能将公平性指标平均提升65%以上，同时仅产生轻微的实用性下降（平均低于4%）。