This paper introduces FairDP, a novel mechanism designed to simultaneously ensure differential privacy (DP) and fairness. FairDP operates by independently training 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, demonstrating improved trade-offs between model utility, privacy, and fairness compared with existing methods.

翻译：本文提出了一种新颖机制 FairDP，旨在同时确保差分隐私与公平性。该机制通过为不同群体独立训练模型，使用针对特定群体的裁剪项来评估并约束差分隐私带来的差异影响。在训练过程中，该机制逐步整合来自群体模型的知识，构建出一个在下游任务中平衡隐私性、效用性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，表明与现有方法相比，其在模型效用、隐私性和公平性之间实现了更优的权衡。