Exploiting Dynamic Privacy in Socially Regularized Recommenders

Users of social networks have shown an increasing concern for exposing their personal data to untrusted entities in order to receive recommendations. In this work, we describe the components of a privacy-aware collaborative filtering based recommender framework which targets two important issues in recommender systems operating in a social network: privacy concern of profile owners and sparsity of social trust among users in a social network. Assuming an initial global privacy in the social network, the framework employs a probabilistic matrix factorization technique to estimate the quality of the missing trust relation between each pair of users. Because of the latent features inferred by matrix factorization, the resulting trust is an augmentation of both social relation and user similarity driven trust. We introduce a privacy inference model which exploits the underlying inter-entity trust information to obtain a personalized privacy view for each individual in the social network. Using this personalized privacy view, we employ an off-the-shelf collaborative filtering recommender system to make predictions. Experimental results show that the proposed approach obtains better accuracy than similar non-privacy aware recommender systems, while at the same time meeting profile privacy concerns

Exploiting Dynamic Privacy in Socially Regularized Recommenders

Users of social networks have shown an increasing concern for exposing their personal data to untrusted entities in order to receive recommendations. In this work, we describe the components of a privacy-aware collaborative filtering based recommender framework which targets two important issues in recommender systems operating in a social network: privacy concern of profile owners and sparsity of social trust among users in a social network. Assuming an initial global privacy in the social network, the framework employs a probabilistic matrix factorization technique to estimate the quality of the missing trust relation between each pair of users. Because of the latent features inferred by matrix factorization, the resulting trust is an augmentation of both social relation and user similarity driven trust. We introduce a privacy inference model which exploits the underlying inter-entity trust information to obtain a personalized privacy view for each individual in the social network. Using this personalized privacy view, we employ an off-the-shelf collaborative filtering recommender system to make predictions. Experimental results show that the proposed approach obtains better accuracy than similar non-privacy aware recommender systems, while at the same time meeting profile privacy concerns