Privacy Management in Social Network Data Publishing with Community Structure

摘要

Due to the background knowledge of the community structure, there are privacy issues with respect to published anonymous social network data. We analyzed the privacy leakage of the k-anonymity model and proposed a privacy protection method with community structure information. We implemented the proposed method by combining k-anonymous and random perturbation techniques. We used three real datasets to evaluate our privacy-preserving method. Experimental results show that our method has the same level of privacy protection as the k-anonymity model, but retains more community structure information.