As LLMs rapidly advance and enter real-world use, their privacy implications are increasingly important. We study an authorship de-anonymization threat: using LLMs to link anonymous documents to their authors, potentially compromising settings such as double-blind peer review. We propose De-Anonymization at Scale (DAS), a large language model-based method for attributing authorship among tens of thousands of candidate texts. DAS uses a sequential progression strategy: it randomly partitions the candidate corpus into fixed-size groups, prompts an LLM to select the text most likely written by the same author as a query text, and iteratively re-queries the surviving candidates to produce a ranked top-k list. To make this practical at scale, DAS adds a dense-retrieval prefilter to shrink the search space and a majority-voting style aggregation over multiple independent runs to improve robustness and ranking precision. Experiments on anonymized review data show DAS can recover same-author texts from pools of tens of thousands with accuracy well above chance, demonstrating a realistic privacy risk for anonymous platforms. On standard authorship benchmarks (Enron emails and blog posts), DAS also improves both accuracy and scalability over prior approaches, highlighting a new LLM-enabled de-anonymization vulnerability.

翻译：随着大语言模型（LLM）的快速发展与进入实际应用，其隐私影响日益重要。我们研究了一种作者身份去匿名化威胁：利用LLM将匿名文档与作者相关联，可能危及双盲同行评审等场景。我们提出大规模去匿名化（DAS）方法，这是一种基于大语言模型的方法，可在数万篇候选文本中实现作者归因。DAS采用顺序递进策略：将候选语料库随机划分为固定大小的组，提示LLM选择与查询文本最可能由同一作者撰写的文本，并通过迭代重新查询存活候选者生成排序前k名列表。为使该方法在大规模场景中实用，DAS引入密集检索预过滤器以缩小搜索空间，并采用多轮独立运行的多数投票式聚合以提升鲁棒性与排序精度。对匿名评审数据的实验表明，DAS能在数万规模的文本池中显著高于随机水平地恢复同作者文本，揭示了匿名平台面临的现实隐私风险。在标准作者归因基准（安然邮件与博客文章）上，DAS相较于现有方法在准确性与可扩展性方面均取得提升，凸显了LLM驱动的新型去匿名化漏洞。