Journal
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
Volume 14, Issue 5, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3608481
Keywords
Neighborhood reuse; differential privacy; collaborative filtering; k nearest neighbors; recommender systems; privacy risk; popularity bias
Ask authors/readers for more resources
In this study, we propose a novel differentially private KNN-based recommender system called ReuseKNN, which reduces privacy risk by identifying highly reusable neighborhoods with small size. Experimental results demonstrate that ReuseKNN outperforms traditional UserKNN in terms of accuracy while protecting a smaller number of neighbors with differential privacy.
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 the recommendations could expose the neighbors' rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors' ratings, which unfortunately 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 with 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available