Deeply Understanding Structure-based Social Network De-anonymization

Abstract

Anonymization techniques are widely adopted to protect users’ privacy during social data publishing and sharing. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors. We design the analysis framework and define three experiment environments to evaluate a few factors’ impacts on the target algorithms. Based on our analysis architecture, we simulate two typical de-anonymization algorithms and evaluate their performance under different pre-configured environments.