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Online Social Networks have gained increased popularity in recent years. However, besides their many advantages, they also represent privacy risks for the users. In order to control access to their private information, users of OSNs are typically allowed to set the visibility of their profile attributes, but this may not be sufficient, because visible attributes, friendship relationships, and group memberships can be used to infer private information. In this paper, we propose a fully automated approach based on machine learning for inferring undisclosed attributes of OSN users. Our method can be used for both classification and regression tasks, and it makes large scale privacy attacks feasible. We also provide experimental results showing that our method achieves good performance in practice.