Personalized Author Obfuscation with Large Language Models

Mohammad Shokri; Sarah Ita Levitan; Rivka Levitan

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arXiv · 2505.12090

Personalized Author Obfuscation with Large Language Models

Mohammad Shokri·Sarah Ita Levitan·Rivka Levitan

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Abstract

In this paper, we investigate the efficacy of large language models (LLMs) in obfuscating authorship by paraphrasing and altering writing styles. Rather than adopting a holistic approach that evaluates performance across the entire dataset, we focus on user-wise performance to analyze how obfuscation effectiveness varies across individual authors. While LLMs are generally effective, we observe a bimodal distribution of efficacy, with performance varying significantly across users. To address this, we propose a personalized prompting method that outperforms standard prompting techniques and partially mitigates the bimodality issue.

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Mohammad Shokri, Sarah Ita Levitan, Rivka Levitan. 2025-05-17. Personalized Author Obfuscation with Large Language Models. https://arxiv.org/abs/2505.12090

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