Style aware paraphrasing reduces text authorship identification risks
I Am No One: Style-Aware Paraphrasing for Text Anonymization
Computation and LanguageCryptography and Security
Summary
Text can reveal who wrote it based on writing style even if names are removed. The authors found a way to rewrite text using AI that changes the style to hide who wrote it, while keeping the meaning clear. This method makes it much harder to guess the author and keeps the text easy to read. It works better than past privacy methods that often made texts hard to understand.
What this means in practice
- •For call center operators: Protect customer and employee privacy by automatically rewriting call transcripts to hide speaker style while preserving conversation content.
- •For online content publishers: Publish user-generated content like blogs or reviews without exposing writers’ identities through their writing style.
Authors
Ahmed Sohair Khan, Estrid He, Monica Wachowicz, Elham Naghizade
Abstract
Authorship 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. Differential privacy-based anonymization often severely degrades text quality and utility. We propose a style-aware, prompt-driven anonymization approach that uses pretrained large language models to construct compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. Across blog and review datasets, our approach reduces authorship attribution F1 by 60-70% while maintaining content quality and readability, substantially outperforming DP-based and non-DP baselines.