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This paper investigates whether LLMs can identify their own model family from stylometric fingerprints in role-constrained political analysis texts, even after prompt-level anonymization. The findings confirm that anonymization is insufficient and have implications for EU AI Act compliance and multi-agent system validation.
AURA is an LLM-powered anonymization framework that balances privacy protection against agentic web-search re-identification while preserving contextual utility through adaptive privacy scopes and mask-reconstruct methods.
This case study empirically investigates where anonymization should be applied in Retrieval-Augmented Generation (RAG) pipelines to balance privacy and utility, examining the impact of anonymization at different stages (dataset vs. generated answer) to inform privacy risk mitigation strategies.