This paper introduces FairDP, a novel mechanism designed to achieve certified fairness with differential privacy (DP). FairDP independently trains models for distinct individual groups, using group-specific clipping terms to assess and bound the disparate impacts of DP. Throughout the training process, the mechanism progressively integrates knowledge from group models to formulate a comprehensive model that balances privacy, utility, and fairness in downstream tasks. Extensive theoretical and empirical analyses validate the efficacy of FairDP and improved trade-offs between model utility, privacy, and fairness compared with existing methods.

翻译：本文提出FairDP，一种旨在实现可认证公平性与差分隐私（DP）协同保障的新机制。FairDP对不同个体组独立训练模型，通过组特定的裁剪项评估并约束差分隐私对群体的差异影响。在训练过程中，该机制逐步整合组模型的知识，构建出一个在下游任务中平衡隐私、效用和公平性的综合模型。广泛的理论与实证分析验证了FairDP的有效性，并表明其相比现有方法在模型效用、隐私和公平性之间实现了更优的权衡。