SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

Abstract

In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented. Previous anonymization algorithms do not consider distribution of nodes in social network graph according to their attributes. SACk tries to focus on this aspect that probability of existence of an edge between two nodes is related to their attributes and this leads to a graph with connected subgraphs. Using connected subgraphs in anonymization process this method obtains better experimental results both in data quality and time. Sequential clustering is used for anonymization using k-edge connected subgraphs for starting step. Sequential clustering is a greedy algorithm and results are dependent on starting point.