Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry
TL;DR
This work identifies a phenomenon termed token-level memorization asymmetry through theoretical analysis of diffusion training dynamics and proposes Q-Skew, a quantile-weighted skewness-based indicator for membership inference on finetuned DLMs.
Abstract
Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel generation and bidirectional context modeling. Despite growing interest in their generative capabilities, the privacy risks of DLMs remain underexplored. We identify a phenomenon termed token-level memorization asymmetry through theoretical analysis of diffusion training dynamics. Building on this finding, we propose Q-Skew, a quantile-weighted skewness-based indicator for membership inference on finetuned DLMs. Experiments across multiple fine-tuning datasets and models show that our method outperforms existing baselines. Moreover, we show that Q-Skew can also facilitate other privacy violations, such as PII extraction. Our findings reveal a previously underexplored privacy attack surface and highlight the need for systematic privacy evaluation of DLMs.
