K-degree closeness anonymity: A centrality measure based approach for network anonymization

Abstract

Social network data are generally published in the form of social graphs which are being used for extensive scientific research. We have noticed that even a k-degree anonymization of social graph can’t ensure protection against identity disclosure. In this paper, we have discussed how closeness centrality measure can be used to identify a social entity in the presence of kdegree anonymization. We have proposed a new model called k-degree closeness anonymization by adopting a mixed strategy of k-degree anonymity, degree centrality and closeness centrality. The model has two phases, namely, construction and validation. The construction phase transforms a graph with given sequence to a graph with anonymous sequence in such a manner that the closeness centrality measure is distributed among the nodes in a smooth way. The nodes with the same degree centrality are assigned with a closer set of closeness centrality values, making re-identification difficult. Validation phase validates our model by generating 1-neighborhood graphs. Algorithms have been developed both for the construction and validation phases.