  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.
