User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors since their rating data might be exposed to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors' ratings, which reduces the accuracy of UserKNN. In this work, we introduce ReuseKNN, a novel differentially-private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy, and (ii) most users do not need to be protected with differential privacy, since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations: Firstly, ReuseKNN requires significantly smaller neighborhoods, and thus, fewer neighbors need to be protected with differential privacy compared to traditional UserKNN. Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN.

翻译：基于用户的KNN推荐系统（UserKNN）在推荐过程中利用目标用户的k个最近邻的评分数据。然而，这会增加邻居的隐私风险，因为其评分数据可能暴露给其他用户或恶意方。为降低此风险，现有工作通过向邻居评分添加随机性来实现差分隐私，但这会降低UserKNN的准确性。本文提出ReuseKNN——一种新型差分隐私KNN推荐系统。其核心思想是识别规模小但可高度复用的邻域，从而：(i) 仅需对最小用户集施加差分隐私保护，(ii) 大多数用户因极少被用作邻居而无需差分隐私保护。我们在五个不同数据集上的实验得出两个关键发现：首先，ReuseKNN所需邻域显著更小，因此与传统UserKNN相比，需受差分隐私保护的邻居数量更少；其次，尽管邻域规模小，ReuseKNN在准确性上仍优于UserKNN和完全差分隐私方法。总体而言，相较于UserKNN，ReuseKNN能显著降低用户的隐私风险。