arXiv Computation and Language
Sep 2

Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry

The paper introduces a new privacy vulnerability in diffusion language models (DLMs) called token‑level memorization asymmetry, derived from theoretical analysis of diffusion training dynamics. It proposes Q‑Skew, a quantile‑weighted skewness indicator, to perform membership inference on fine‑tuned DLMs, outperforming existing baselines across multiple datasets and models. Additionally, Q‑Skew can be used to extract personally identifiable information (PII), demonstrating a broader privacy attack surface.

By Shengfang Zhai, Leo Marchyok, Yuling Shi, Huanran Chen, Yinpeng Dong, Jiaheng Zhang, Sanghyun Hong
arXiv AI
Sep 4

User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios

The study investigates how users perceive the helpfulness and privacy-preservation of large language model (LLM) responses in privacy-sensitive scenarios. Using 94 participants and 90 PrivacyLens scenarios, researchers found that users’ evaluations of identical LLM outputs varied widely, whereas five proxy LLM judges showed high agreement but low correlation with user judgments. The results suggest that proxy LLMs cannot reliably estimate users’ diverse perceptions of utility and privacy, highlighting the need for more user-centered evaluation methods.

By Xiaoyuan Wu, Roshni Kaushik, Wenkai Li, Lujo Bauer, Koichi Onoue