AI RESEARCH
Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Text
arXiv CS.LG
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ArXi:2603.17217v1 Announce Type: cross Responsible use of AI demands that we protect sensitive information without undermining the usefulness of data, an imperative that has become acute in the age of large language models. We address this challenge with an on-premise, LLM-driven substitution pipeline that anonymizes text by replacing personally identifiable information (PII) with realistic, type-consistent surrogates. Executed entirely within organizational boundaries using local LLMs, the approach prevents data egress while preserving fluency and task-relevant semantics.