← Back to all news
arXiv Machine Learning By Sinan Y{\i}ld{\i}r{\i}m, Megha Khosla

Bayesian Membership Privacy for Graph Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2606. 04069v1 Announce Type: cross Abstract: Existing privacy analyses for Graph Neural Networks (GNNs) largely inherit assumptions from non-graph settings, overlooking structural correlations and stochastic training-graph sampling.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 23

Empirical Auditing of Edge-Private Graph Generators

The paper presents an empirical audit of privacy leakage in edge‑private graph generators by testing whether outputs from edge‑neighbouring inputs remain distinguishable. It introduces statistically valid lower bounds on privacy loss and compares direct‑edge, local‑structural, and GNN‑based attacks based on the geometry around a target edge. Experiments on two generators and two networks reveal that privacy leakage varies with both the mechanism and the network, and that learned representations expose information beyond conventional local statistics.

By Anum Fatima, Stratis Limnios, James Adams, Lukasz Szpruch, Carsten Maple, Gesine Reinert, Andrew Elliott
safety
More like this →
arXiv Machine Learning
Aug 27

Are LLM-Enhanced GNNs Privacy-Safe?

The paper evaluates privacy risks in graph neural networks enhanced by large language models (LLMs). Using a five‑stage framework, the authors test six real‑world text‑attributed graph datasets with 42 model configurations and six privacy attack methods across link, label, and membership inference threats. Results show that LLM‑enhanced GNNs are more vulnerable than shallow baselines, with semantic enrichment amplifying exploitable signals, and that differential privacy can reduce risk but at a significant cost to utility.

By Longzhu He, Zelang Wen, Chaozhuo Li, Sen Su
llmsragsafety
More like this →
arXiv Machine Learning
Jul 10

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy

arXiv:2607. 08659v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information.

By Wenxiu Ding, Muzhi Liu, Zheng Yan, Mingjun Wang, Yifan Zhao, Qiao Liu
efficiencybenchmarkssafety
More like this →