Sanitization Techniques for Protecting Social Networks from Inference Attacks?

Social networking applications became popular as they provide virtual platform that can help in making people of all walks of life to have various communication channels over Internet. These applications allow users to publish their data and share it with other users as well. In the process, it is possible that adversaries can launch private information inference attacks to know sensitive information. In this paper we focus on finding how such applications are vulnerable to private information inference attacks and devise preventing mechanisms. We employ different sanitization techniques that prevent private information inference attacks. The sensitive information is thus prevented from being disclosed to unauthorized people. We built a prototype system that can demonstrate the proof of concept. We used the Census dataset in order to test the application. The empirical results are encouraging.