Privacy Preference Inference via Collaborative Filtering

Vol. 10 No. 1 (2016): Tenth International AAAI Conference on Web and Social Media

Volume

Vol. 10 No. 1 (2016): Tenth International AAAI Conference on Web and Social Media

Poster Papers

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.

10.1609/icwsm.v10i1.14770