LLM Anonymization Against Agentic Re-Identification

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 (\textbf{A}nonymization with \textbf{U}tility-\textbf{R}etention \textbf{A}daptation), an LLM-powered \textit{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 adaptive-scope AURA yields the lowest agentic re-identification counts under each of three attacker models among the non-DP methods, and that at matched scope and backbone, AURA's mask-reconstruct design retains more contextual utility than the prior LLM anonymizer (+6.4 pp unit-grid recovery) at comparable privacy. Source Code: this https URL

Cryptography and Security (cs.CR); Computation and Language (cs.CL)

arXiv:2605.30848 [cs.CR]

(or arXiv:2605.30848v3 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2605.30848 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.