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。为此，常通过向数据中添加随机噪声来实现差分隐私保护。然而，目前尚不明确这种机制如何影响个性化推荐。本研究探究了将差分隐私应用于最新推荐模型训练数据时，其对推荐准确度与流行度偏差的影响。我们获得三项发现：第一，应用差分隐私后，几乎所有用户的推荐结果均发生改变；第二，推荐准确度显著下降，而推荐物品的流行度急剧上升，表明流行度偏差加剧；第三，相较于偏好流行物品的用户，差分隐私对偏好冷门物品用户的流行度偏差影响更为严重。