Utility-Oriented K-Anonymization on Social Networks

Abstract

“Identity disclosure” problem on publishing social network data has gained intensive focus from academia. Existing k-anonymization algorithms on social network may result in nontrivial utility loss. The reason is that the number of the edges modified when anonymizing the social network is the only metric to evaluate utility loss, not considering the fact that different edge modifications have different impact on the network structure. To tackle this issue, we propose a novel utility-oriented social network anonymization scheme to achieve privacy protection with relatively low utility loss. First, a proper utility evaluation model is proposed. It focuses on the changes on social network topological feature, but not purely the number of edge modifications. Second, an efficient algorithm is designed to anonymize a given social network with relatively low utility loss. Experimental evaluation shows that our approach effectively generates anonymized social network with high utility.