An Efficient and Robust Social Network De-anonymization Attack

Gábor György Gulyás (1) , Benedek Simon (2) , Sándor Imre (2)

1 PRIVATICS - Privacy Models, Architectures and Tools for the Information Society
2 BME - Budapest University of Technology and Economics [Budapest]

Releasing connection data from social networking services can pose a significant threat to user privacy. In our work, we consider structural social network de-anonymization attacks , which are used when a malicious party uses connections in a public or other identified network to re-identify users in an anonymized social network release that he obtained previously. In this paper we design and evaluate a novel social de-anonymization attack. In particular, we argue that the similarity function used to re-identify nodes is a key component of such attacks, and we design a novel measure tailored for social networks. We incorporate this measure in an attack called Bumblebee. We evaluate Bumblebee in depth, and show that it significantly outperforms the state-of-the-art, for example it has higher re-identification rates with high precision, robustness against noise, and also has better error control.

Mots clés: de-anonymization; anonymity; social network; privacy

Domaines: Cryptographie et sécurité [cs.CR]

Gábor György Gulyás, Benedek Simon, Sándor Imre. An Efficient and Robust Social Network De-anonymization Attack. Workshop on Privacy in the Electronic Society, Oct 2016, Vienna, Austria. ⟨10.1145/2994620.2994632⟩. ⟨hal-01380768⟩