Large-scale online deanonymization with LLMs

Researchers demonstrate that large language models can successfully deanonymize pseudonymous users across online platforms at scale, achieving up to 68% recall at 90% precision. The study shows LLMs can match users between platforms like Hacker News and LinkedIn, or across Reddit communities, using only unstructured text data.

• →LLMs can deanonymize users across platforms with high precision using only pseudonymous profiles and conversations.
• →The attack pipeline extracts identity features, searches for matches via semantic embeddings, and verifies candidates to reduce false positives.
• →LLM-based methods achieved up to 68% recall at 90% precision compared to near 0% for classical non-LLM approaches.
• →The research demonstrates that practical obscurity protecting pseudonymous online users no longer provides adequate privacy protection.
• →Threat models for online privacy need to be fundamentally reconsidered given these new deanonymization capabilities.