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.

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Author Of this post: <a href="">Zhenhuan Yang</a>, <a href="">Yingqiang Ge</a>, <a href="">Congzhe Su</a>, <a href="">Dingxian Wang</a>, <a href="">Xiaoting Zhao</a>, <a href="">Yiming Ying</a>

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