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Collaborative filtering-based recommender systems leverage vast amounts ofbehavioral user data, which poses severe privacy risks. Thus, often, randomnoise is added to the data to ensure Differential Privacy (DP). However, todate, it is not well understood, in which ways this impacts personalizedrecommendations. In this work, we study how DP impacts recommendation accuracyand popularity bias, when applied to the training data of state-of-the-artrecommendation models. Our findings are three-fold: First, we find that nearlyall users' recommendations change when DP is applied. Second, recommendationaccuracy drops substantially while recommended item popularity experiences asharp increase, suggesting that popularity bias worsens. Third, we find that DPexacerbates popularity bias more severely for users who prefer unpopular itemsthan for users that prefer popular items.

The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias