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With the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook. Current online social networks have designed some strategies to protect users' privacy, but they are not stringent enough. Some public information of profile or relationship can be utilized to infer users' private information. Online social networks usually contain little public available information of users (labeled data) but with a large number of hidden ones (unlabeled data). Recently, Semi-Supervised Learning (SSL), which has the advantage of utilizing fewer labeled data to achieve better performance compared to classical Supervised Learning, attracts much attention from the web research community with a massive set of unlabeled data. In our paper, we focus on the privacy issue of online social networks, which is a hot and dynamic research topic. More specifically, we propose a novel SSL framework that can be used to exploit security issues in online social networks. We first introduce the general SSL framework and outline two exploit models with associated strategies within it, e.g., graph-based models and co-training model. Finally, we conduct a series of experiments on real-world data from Facebook and StudiVZ to evaluate the effectiveness of this SSL exploit framework. Experimental results demonstrate that our approaches can accurately infer sensitive information of online users and more effective compared to previous models.