Privacy Risk in Graph Stream Publishing for Social Network Data

Abstract

To understand how social networks evolve over time, graphs representing the networks need to be published periodically or on-demand. The identity of the participants (nodes) must be anonymized to protect the privacy of the individuals and their relationships (edges) to the other members in the social network. We identify a new form of privacy attack, which we name the degree-trail attack. This attack re-identifies the nodes belonging to a target participant from a sequence of published graphs by comparing the degree of the nodes in the published graphs with the degree evolution of a target. The power of this attack is that the adversary can actively influence the degree of the target individual by interacting with the social network. We show that the adversary can succeed with a high probability even if published graphs are anonymized by strongest known privacy preserving techniques in the literature. Moreover, this success does not depend on the distinctiveness of the target nodes nor require the adversary to behave differently from a normal participant. One of our contributions is a formal method to assess the privacy risk of this type of attacks and empirically study the severity on real social network data.