Privacy Preference Inference via Collaborative Filtering

Abstract

Studies of online social behaviour indicate that users often fail to specify privacy settings that match their privacy behaviour. This issue has caused a dilemma whether to use publicly available data for targeted advertisement and personalization. As a possible approach to manage this dilemma, we propose a collaborative filtering method that exploits homophily to build a probabilistic model. Such a model can indicate the likelihood that a given public profile is meant to be private. Here, we provide the results of an analysis of a set of observable variables to be used in a neighbourhood-based manner. In addition, we establish a social graph augmented with privacy information. Users in the graph are then transformed into a set of latent features, uncovering informative factors to infer privacy preferences.