Anonymizing approach to resist label-neighborhood attacks in dynamic releases of social networks

Abstract

Data collection by social networking applications offers many opportunities for mining information, which provides a better understanding of social structures and their dynamic structures. Anonymization of social networks before they are published or shared is particularly important, since social network data usually contain much sensitive information on individuals. In this paper, we address the privacy problems of dynamic releases of social networks. We re-define the label-neighborhood attack model in dynamic social network releases. An adversary can use one-hop neighbor's network structure and label as background knowledge to identity the victim to learn more sensitive information. We propose a dynamic-l-diversity anonymized method to resist attacks. Experiments show that the proposed approach can retain much of the characteristics of the network while providing high utility.