Inference attack prevention of private information on social Networks

Online social networks, such as Facebook, Linked In are increasingly use by many people. These networks allow users to give details about themselves and allow connecting to their friends. Some of the information visible inside these networks is meant to be private. It is possible to use learning algorithms on released data to predict private information. We explore how to launch inference attacks using released social networking data to predict private information. We devise three possible sanitization techniques that could be used in various situations. Then, we define the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. We also show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described.