Preservation of Privacy in Publishing Social Network Data

Abstract

This paper consider the privacy disclosure in social network data publishing. We assume that adversaries know the degree of a target individual and the target's immediate neighbors, and identify an essential type of privacy attacks: background knowledge attacks. We propose a practical solution to defend against background knowledge attacks. The experimental results confirm that the anonymized social networks obtained by our method can still be used to answer aggregate network queries with high accuracy.