Poincar\'e Differential Privacy for Hierarchy-Aware Graph Embedding

arXiv:2312.12183v3 Announce Type: replace
Abstract: Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy is introduced as a new inductive bias into the Graph Neural Networks (GNNs) in various tasks, it implies latent topological relations for attackers to improve their inference attack performance, leading to serious privacy leakage issues. In addition, existing privacy-preserving frameworks suffer from reduced protection ability in …

abstract arxiv behavior bias cs.cr cs.lg differential differential privacy embedding gnns graph graph neural networks graphs human inductive networks neural networks privacy property relationships tasks type world