De-Anonymization at Scale via Tournament-Style Attribution

• URL: http://arxiv.org/abs/2601.12407v1
• Date: Sun, 18 Jan 2026 13:49:43 GMT
• Title: De-Anonymization at Scale via Tournament-Style Attribution
• Authors: Lirui Zhang, Huishuai Zhang,
• Abstract summary: De-Anonymization at Scale (DAS) is a large language model-based method for attributing authorship among tens of thousands of candidate texts.<n>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.
• Score: 15.47801233755864
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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