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Social networks are of significant importance in various application domains. Most previous studies are focused on revealing interesting properties of networks and discovering efficient and effective analysis methods. However, there has been little work dedicated to privacy preserving social network analysis. In this project, we investigate the application of graph perturbation techniques to protect privacy of individual nodes and their sensitive link relationships. We conduct theoretical study and empirical evaluation on the tradeoff between utility and privacy of various graph randomization techniques as well as investigation of various potential attacking methods from adversaries. To quantify the utility loss, we focus on the change of the spectrum and eigenvectors since they have inherent relation with many real space graph characteristics. We expect to develop some spectrum/utility preserving randomization techniques which can better preserve graph utility without sacrificing much privacy protection.
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