# An Efficient and Robust Social Network De-anonymization Attack

arXiv: https://arxiv.org/abs/1610.04064 · HTML: https://mlsanity.com/paper/1610.04064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study](https://mlsanity.com/paper/2310.00221/) (2023)

## Abstract (arXiv)

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.