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It is important to study the risks of publishing privacy-sensitive data. Evenif sensitive identities (e.g., name, social security number) were removed andadvanced data perturbation techniques were applied, several de-anonymizationattacks have been proposed to re-identify individuals. However, existingattacks have some limitations: 1) they are limited in de-anonymizationaccuracy; 2) they require prior seed knowledge and suffer from the imprecisionof such seed information.  We propose a novel structure-based de-anonymization attack, which does notrequire the attacker to have prior information (e.g., seeds). Our attack isbased on two key insights: using multi-hop neighborhood information, andoptimizing the process of de-anonymization by exploiting enhanced machinelearning techniques. The experimental results demonstrate that our method isrobust to data perturbations and significantly outperforms the state-of-the-artde-anonymization techniques by up to $10\times$ improvement.

Blind De-anonymization Attacks using Social Networks