  Graph Neural Networks (GNNs) are powerful tools for learning representations
on graphs, such as social networks. However, their vulnerability to privacy
inference attacks restricts their practicality, especially in high-stake
domains. To address this issue, privacy-preserving GNNs have been proposed,
focusing on preserving node and/or link privacy. This work takes a step back
and investigates how GNNs contribute to privacy leakage. Through theoretical
analysis and simulations, we identify message passing under structural bias as
the core component that allows GNNs to \textit{propagate} and \textit{amplify}
privacy leakage. Building upon these findings, we propose a principled
privacy-preserving GNN framework that effectively safeguards both node and link
privacy, referred to as dual-privacy preservation. The framework comprises
three major modules: a Sensitive Information Obfuscation Module that removes
sensitive information from node embeddings, a Dynamic Structure Debiasing
Module that dynamically corrects the structural bias, and an Adversarial
Learning Module that optimizes the privacy-utility trade-off. Experimental
results on four benchmark datasets validate the effectiveness of the proposed
model in protecting both node and link privacy while preserving high utility
for downstream tasks, such as node classification.
