P-Sensitive K-Anonymity for Social Networks

— The proliferation of social networks, where individuals share private information, has caused, in the last few years, a growth in the volume of sensitive data being stored in these networks. As users subscribe to more services and connect more with their friends, families, and colleagues, the desire to both protect the privacy of the network users and the temptation to extract, analyze, and use this information from the networks have increased. Previous research has looked at anonymizing social network graphs to ensure their k-anonymity in order to protect their nodes against identity disclosure. In this paper we introduce an extension to this k-anonymity model that adds the ability to protect against attribute disclosure. This new model has similar privacy features with the existing p-sensitive k-anonymity model for microdata. We also present a new algorithm for enforcing p-sensitive k-anonymity on social network data based on a greedy clustering approach. To our knowledge, no previous research has been done to deal with preventing against disclosing attribute information that is associated to social networks nodes.