arXiv AI
5d ago

Rank-Aware Differentially Private Release of Listwise Preferences for LLM Alignment

The paper introduces a rank‑aware exponential mechanism that privatizes listwise preference data for large language model alignment under local differential privacy. By sampling a fixed‑size binary partition, the method concentrates noise near borderline items rather than uniformly across all items, thereby reducing sensitivity. Experiments demonstrate that this approach outperforms existing LDP baselines on downstream alignment tasks while maintaining the same privacy budget.

By Junwei Chen, Manjiang Yu, Pengpeng Qiao, Yang Cao
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