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 reconstruct real-world identities, sometimes even during benign tasks. We evaluate this risk across three settings -- classical linkage incidents, a controlled benchmark (\emph{InferLink}) that varies
The increasing sophistication and widespread deployment of LLM agents enable them to perform inference-driven de-anonymization with greater efficacy than previous methods.
This research highlights a significant and emergent privacy risk posed by advanced AI, potentially undermining traditional privacy-preserving measures and impacting data security policies.
The barrier for re-identifying individuals from seemingly anonymized data is significantly lowered, requiring a re-evaluation of data anonymization practices and privacy regulations.
- · Cybersecurity firms specializing in AI-driven privacy protection
- · Regulatory bodies focused on data privacy
- · Individuals with privacy expectations
- · Organizations handling anonymized user data
- · Current anonymization techniques
Increased scrutiny and potential restructuring of data anonymization methodologies across industries.
New legal frameworks and compliance requirements emerge to address AI-driven de-anonymization risks.
Public distrust in data anonymization leads to a demand for 'privacy-by-design' principles in all AI applications.
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Read at arXiv cs.AI