Differentially private graph publishing and randomized response for collaborative filtering

Abstract

Several methods for providing edge and node-differential privacy for graphs have been devised. However, most of them publish graph statistics, not the edge-set of the randomized graph. We present a method for graph randomization that provides randomized response and allows for publishing differentially private graphs. We show that this method can be applied to sanitize data to train collaborative filtering algorithms for recommender systems. Our results afford plausible deniability to users in relation to their interests, with a controlled probability predefined by the user or the data controller. We show in an experiment with Facebook Likes data and psychodemographic profiles, that the accuracy of the profiling algorithms is preserved even when they are trained with differentially private data. Finally, we define privacy metrics to compare our method for different parameters of ε with a k-anonymization method on the MovieLens dataset for movie recommendations.