An Efficient and Robust Social Network De-anonymization Attack
This addresses privacy threats for users of social networking services by improving de-anonymization attacks, representing an incremental advance over existing methods.
The paper tackles the problem of user privacy in social networks by designing a novel de-anonymization attack called Bumblebee, which uses a tailored similarity measure to re-identify users and significantly outperforms state-of-the-art methods with higher re-identification rates, precision, robustness against noise, and better error control.
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.