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Social networking sites such as Orkut, Tribe, or Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed private information. To find the patterns of information revelation and their security implications, the authors analyze the online behavior of wiki-vote data set and evaluate the amount of information they disclose and study their dimension used for reduction. In this paper they conclude that dimension reduction is one of the factors through they can achieve the security and maintain the integrity of dataset.
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