2 papers
cs.CR2026
De-Anonymization at Scale via Tournament-Style Attribution
Lirui Zhang, Huishuai Zhang
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 anonymou…
cs.CL2024
AIDBench: A benchmark for evaluating the authorship identification capability of large language models
Zichen Wen, Dadi Guo, Huishuai Zhang
As large language models (LLMs) rapidly advance and integrate into daily life, the privacy risks they pose are attracting increasing attention. We focus on a specific privacy risk…