Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, trained by differentially private stochastic gradient descent (DP-SGD), results in a disparate impact on user groups with respect to different user engagement levels. This, in turn, causes the original unfair model to become even more biased against inactive users. To address the above issues, we propose \textbf{DP-Fair}, a two-stage framework for collaborative filtering based algorithms. Specifically, it combines differential privacy mechanisms with fairness constraints to protect user privacy while ensuring fair recommendations. The experimental results, based on Amazon datasets, and user history logs collected from Etsy, one of the largest e-commerce platforms, demonstrate that our proposed method exhibits superior performance in terms of both overall accuracy and user group fairness on both shallow and deep recommendation models compared to vanilla DP-SGD.

翻译：近年来，差分隐私引导的算法在隐私保护的机器学习任务中得到日益广泛的应用。然而，此类算法的使用会带来算法公平性方面的权衡问题，这已得到普遍认可。具体而言，我们通过实验观察到，采用差分隐私随机梯度下降（DP-SGD）训练的经典协同过滤方法，会对不同用户参与度的用户群体产生差异性影响。这进而导致原本不公平的模型对不活跃用户产生更严重的偏见。为解决上述问题，我们提出\textbf{DP-Fair}——一种面向协同过滤算法的两阶段框架。该框架将差分隐私机制与公平性约束相结合，在保护用户隐私的同时确保推荐结果的公平性。基于Amazon数据集和全球最大电子商务平台之一Etsy的用户历史日志的实验结果表明，与原始DP-SGD相比，我们提出的方法在浅层和深度推荐模型上，在整体准确率和用户群体公平性方面均展现出更优的性能。