Anonymizing Social Networks

Advances in technology have made it possible to collect data about individuals and the connections between them, such as email correspondence and friendships. Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data. However, in many cases the data describes relationships that are private (e.g., email correspondence) and sharing the data in full can result in unacceptable disclosures. In this paper, we present a framework for assessing the privacy risk of sharing anonymized network data. This includes a model of adversary knowledge, for which we consider several variants and make connections to known graph theoretical results. On several real-world social networks, we show that simple anonymization techniques are inadequate, resulting in substantial breaches of privacy for even modestly informed adversaries. We propose a novel anonymization technique based on perturbing the network and demonstrate empirically that it leads to substantial reduction of the privacy threat. We also analyze the effect that anonymizing the network has on the utility of the data for social network analysis.