# I Am No One: Style-Aware Paraphrasing for Text Anonymization

arXiv: https://arxiv.org/abs/2609.12341 · HTML: https://mlsanity.com/paper/2609.12341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [Locally Differentially Private Document Generation Using Zero Shot Prompting](https://mlsanity.com/paper/2310.16111/) (2023)
- [ER-AE: Differentially Private Text Generation for Authorship Anonymization](https://mlsanity.com/paper/1907.08736/) (2019)
- [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://mlsanity.com/paper/2202.13469/) (2022)
- [DP-MLM: Differentially Private Text Rewriting Using Masked Language Models](https://mlsanity.com/paper/2407.00637/) (2024)
- [StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://mlsanity.com/paper/2408.15666/) (2024)
- [Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting](https://mlsanity.com/paper/2410.00751/) (2024)
- [JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models](https://mlsanity.com/paper/2402.08761/) (2024)
- [ALISON: Fast and Effective Stylometric Authorship Obfuscation](https://mlsanity.com/paper/2402.00835/) (2024)

## Abstract (arXiv)

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.