Exploit of Online Social Networks with Semi-Supervised Learning
Refereed conference paper presented and published in conference proceedings

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AbstractWith the rapid growth of the Internet, more and more people interact with their friends in online social networks like Facebook(1). 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(2) 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.
All Author(s) ListMo MZ, Wang DY, Li BC, Hong D, King IW
Name of ConferenceWorld Congress on Computational Intelligence (WCCI 2010)
Start Date of Conference01/01/2010
Place of ConferenceBarcelona
Country/Region of ConferenceSpain
Detailed descriptionorganized by IEEE,
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic

Last updated on 2021-19-02 at 23:55