Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is not well understood, in which ways this impacts personalized recommendations. In this work, we study how DP impacts recommendation accuracy and popularity bias, when applied to the training data of state-of-the-art recommendation models. Our findings are three-fold: First, we find that nearly all users' recommendations change when DP is applied. Second, recommendation accuracy drops substantially while recommended item popularity experiences a sharp increase, suggesting that popularity bias worsens. Third, we find that DP exacerbates popularity bias more severely for users who prefer unpopular items than for users that prefer popular items.

翻译：基于协同过滤的推荐系统依赖大量用户行为数据，这带来了严重的隐私风险。因此，通常会在数据中添加随机噪声以实现差分隐私（DP）。然而，目前尚不清楚这对个性化推荐的具体影响。本研究探讨了将DP应用于先进推荐模型训练数据时，其对推荐准确性和流行度偏差的影响。我们的发现主要有三点：第一，应用DP后几乎所有用户的推荐结果都会发生变化；第二，推荐准确性显著下降，同时推荐项目的流行度急剧上升，表明流行度偏差加剧；第三，我们发现DP对偏好冷门项目的用户造成的流行度偏差加剧程度，比对偏好热门项目的用户更为严重。