Hiding information against structural re-identification

Abstract

Connections between users of social networking services pose a significant privacy threat. Recently, several social network de-anonymization attacks have been proposed that can efficiently re-identify users at large scale, solely considering the graph structure. In this paper, we consider these privacy threats and analyze de-anonymization attacks at the model level against a user-controlled privacy-enhancing technique called identity separation. The latter allows creating seemingly unrelated identities in parallel, even without the consent of the service provider or other users. It has been shown that identity separation can be used efficiently against re-identification attacks if user cooperate with each other. However, while participation would be crucial, this cannot be granted in a real-life scenario. Therefore, we introduce the y-identity model, in which the user creates multiple separated identities and assigns the sensitive attribute to one of them according to a given strategy. For this, we propose a strategy to be used in real-life situations and formally prove that there is a higher bound for the expected privacy loss which is sufficiently low.