10 papers
Privacy Leakage from a Thousand Words: Millipixel Location Recovery from Dot Maps
Yuntao Du, Tanishq Pauskar, Hao Wang +2
Dot maps, which visualize individual data points as dots over a geographic region, are widely used across diverse domains to represent spatial patterns in sensitive data. However,…
AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models
Yuntao Du, Minh Dinh, Kaiyuan Zhang +1
Scientific and Technical Intelligence (S&TI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to bridge the ve…
Membership Inference Attacks Against Fine-tuned Diffusion Language Models
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +5
Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction. Yet their susceptibility to priva…
Window-based Membership Inference Attacks Against Fine-tuned Large Language Models
Yuetian Chen, Yuntao Du, Kaiyuan Zhang +4
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes…
Membership Inference Attacks on Tokenizers of Large Language Models
Meng Tong, Yuntao Du, Kejiang Chen +2
Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks are applied to pre-trained larg…
Beyond Data Privacy: New Privacy Risks for Large Language Models
Yuntao Du, Zitao Li, Ninghui Li +1
Large Language Models (LLMs) have achieved remarkable progress in natural language understanding, reasoning, and autonomous decision-making. However, these advancements have also c…