TKDA: An Improved Method for K-degree Anonymity in Social Graphs

Abstract

Data anonymization is one of the most important directions in privacy-preserving. However, research shows that simple anonymization of data does not protect privacy. To solve this problem, we present a novel and effective algorithm named tree-based K-degree anonymity (TKDA). We devise a new anonymity sequence generation method to reduce the information loss for social graphs. Then, the dynamic anonymization process is implemented by a depth-first search (DFS) traversal algorithm. Finally, the graph modification algorithm based on the anonymous sequence can keep the original graph structure stable. Average Path Length (APL), Average Clustering Coefficient (ACC), and Transitivity (T) are employed to evaluate the method. Experimental results on several datasets show that TKDA is closer to the values of the original graphs on the correlated three experimental metrics, which indicates that TKDA portrays the real data in more detail and improves the utility of the released data.