Author

Ruixuan Liu

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Preprint Oct 2026

Auditing the Privacy of Synthetic Gene Expression Data: A Unified Weighted-Distance Framework for No-Box Membership Inference

Synthetic gene expression data is increasingly proposed as a privacy-preserving substitute for controlled-access genomic repositories, but its safety depends on empirical auditing. Membership inference attacks (MIAs) provide that audit by testing whether a patient's gene expression profile was used to train a generativ...

Owen Tucker, Lily Wang, Harutoshi Okumura et al. · 0 citations

2026

DP2-RAG: An Efficient Full-Process Differential Privacy Implementation in Retrieval-Augmented Generation

As Retrieval-Augmented Generation (RAG) brings Large Language Models (LLMs) into contact with proprietary and user-specific knowledge bases in sensitive domains, protecting the privacy of both queries and knowledge content becomes a critical requirement. The RAG pipeline, however, exposes sensitive information at two d...

Yuxuan Cai, Chao-Xiang He, Hong-Sheng Hu et al. · IEEE Transactions on Informa... · 0 citations

Preprint Aug 2026

PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration

Experiments on four benchmarks for node classification and link prediction show that PriDyG consistently outperforms geometrically decaying baselines under the same privacy budget and matches the utility of naive per-update retraining while reducing cumulative privacy cost by up to three orders of magnitude.

Yuyang Xia, Ruixuan Liu, Li Xiong · 0 citations