From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents
A new study reveals that LLM-based agents can autonomously reconstruct real-world identities from scattered, non-identifying cues by combining them with public information. These findings establish identity inference as a first-class privacy risk, significantly outperforming classical de-anonymization methods.
Computer Science > Artificial Intelligence
Title:From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents
View PDF HTML (experimental)Abstract:Anonymization is widely treated as a practical safeguard because re-identifying anonymous records was historically costly, requiring domain expertise, tailored algorithms, and manual corroboration. We study a growing privacy risk that may weaken this barrier: LLM-based agents can autonomously reconstruct real-world identities from scattered, individually non-identifying cues. By combining these sparse cues with public information, agents resolve identities without bespoke engineering. We formalize this threat as \emph{inference-driven linkage} and systematically evaluate it across three settings: classical linkage scenarios (Netflix and AOL), \emph{InferLink} (a controlled benchmark varying task intent, shared cues, and attacker knowledge), and modern text-rich artifacts. Without task-specific heuristics, agents successfully execute both fixed-pool matching and open-ended identity resolution. In the Netflix Prize setting, an agent reconstructs 79.2% of identities, significantly outperforming a 56.0% classical baseline. Furthermore, linkage emerges not only under explicit adversarial prompts but also as a byproduct of benign cross-source analysis in \emph{InferLink} and unstructured research narratives. These findings establish that identity inference -- not merely explicit information disclosure -- must be treated as a first-class privacy risk; evaluations must measure what identities an agent can infer.
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Source: arXiv cs.AI Recent
