| title | LLM Anonymization Against Agentic Re-Identification |
|---|---|
| authors | Ziwen Li, Jianing Wen, Tianshi Li |
| date | 2026-06-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.30848 |
| affiliation | Northeastern PEACH Lab |
| links | abs pdf hf code |
| abstract | Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-identification, yet those same details also carry downstream analytic value of the text. Existing defenses either remove explicit identifiers, perturb text for formal privacy, or test rewritten text against non-web inference models, leaving underexplored the operating region between resistance to agentic web-search re-identification and utility retention. We introduce AURA (Anonymization with Utility-Retention Adaptation), an LLM-powered mask-reconstruct framework that decouples privacy localization from utility-preserving reconstruction and selects candidates with adversarial privacy and utility-retention checks. We evaluate AURA on real-user interview transcripts using re-identification attacks carried out by web-search agents, along with a utility evaluation based on interviewee-profile facts, codebook facts, and the joint contextual utility grid. Our results show that AURA improves the privacy-utility frontier by using adaptive privacy scope to strengthen resistance to agentic re-identification and using a mask-reconstruct anonymization method to better preserve contextual utility under fixed privacy scope. |
cite
@misc{li2026llm,
title={LLM Anonymization Against Agentic Re-Identification},
author={Ziwen Li and Jianing Wen and Tianshi Li},
year={2026},
eprint={2605.30848},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.30848}
}