From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents 事件

From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents arXiv:2603.18382v2 Announce Type: replace Abstract: Anonymization is often assumed to protect privacy once explicit identifiers are removed, because re-identification has historically required specialized expertise, tailored algorithms, and manual corroboration. We show that LLM-based agents weaken this barrier: by combining scattered, individually non-identifying cues with public evidence, they recons