New Bayesian Privacy Framework for Graph Neural Networks

Researchers have introduced Bayesian Membership Privacy (BMP), a new framework for assessing privacy in Graph Neural Networks (GNNs). BMP accounts for structural correlations and stochastic training-graph sampling, which are often overlooked in existing privacy analyses. By framing membership inference as a Bayesian hypothesis test, BMP quantifies privacy based on posterior membership probability and offers a more granular insight into privacy leakage than global attack accuracy. AI

影响 Introduces a novel privacy auditing mechanism for GNNs, potentially improving data protection in graph-based AI applications.

排序理由 The cluster contains an academic paper introducing a new privacy framework for GNNs. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Bayesian Membership Privacy
• Graph Neural Networks

• 论文
• 安全

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →