From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents
#de-anonymization #LLM agents #privacy risks #inference #anonymization #AI security #identity inference
📌 Key Takeaways
- LLM agents can infer real identities from minimal, anonymized data through inference-driven de-anonymization.
- The study evaluates the effectiveness of de-anonymization techniques using weak cues in LLM contexts.
- Research highlights privacy risks in AI systems where agents reconstruct identities from seemingly safe information.
- Findings suggest current anonymization methods may be insufficient against advanced inference capabilities of LLMs.
📖 Full Retelling
arXiv:2603.18382v1 Announce Type: new
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 i
🏷️ Themes
Privacy, AI Security
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Original Source
arXiv:2603.18382v1 Announce Type: new
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 i
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