I Am No One: Style-Aware Paraphrasing for Text Anonymization
Prompt-driven style-aware paraphrasing with LLMs cuts authorship attribution F1 by 60-70% while preserving content quality.
The paper proposes a style-aware, prompt-driven anonymization approach using pretrained LLMs to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It addresses stylometric re-identification risks in anonymized text, including ASR transcripts of meetings and call-center calls where leakage persists after acoustic anonymization. Across blog and review datasets, the approach reduces authorship attribution F1 by 60-70%, substantially outperforming both DP-based and non-DP baselines while maintaining readability.
- LLM-built stylistic profiles guide paraphrasing that suppresses identifiable style markers
- Reduces authorship attribution F1 by 60-70% on blog and review datasets
- Outperforms differential-privacy and non-DP anonymization baselines on quality and readability
- Applies to speech-derived text such as ASR transcripts of meetings and call-center audio
From the original article
1 min read at arxiv.orgAuthorship attribution models can re-identify users from seemingly anonymized text by exploiting stable stylistic fingerprints, even after explicit identifiers are removed, posing a growing privacy risk for text publishing and analytics. This risk extends to speech-derived text such as ASR transcripts of meetings and call-center conversations, where stylometric leakage can persist even after acoustic anonymization.
By Ahmed Sohair Khan · arxiv.org
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