Can Diffusion-Based Posterior Smoothing Secure GNNs from Membership Inference Attacks?

Abstract

Graph Neural Networks (GNNs) are increasingly applied in domains such as drug discovery, medical diagnostics, and community detection. Despite their powerful predictive capabilities, GNNs remain vulnerable to privacy threats, particularly in sensitive contexts like healthcare and finance where data confidentiality is paramount. This study targets a pressing security concern in GNNs by addressing the risk of data exposure through Membership Inference Attacks (MIA). We propose a novel, model agnostic defense framework based on Diffusion based Posterior Smoothing (DPS), which enhances the privacy of node level predictions by perturbing class probabilities through a learned diffusion denoising process. Unlike prior methods, DPS maintains the predictive utility of the GNN while significantly reducing the information leakage exploited by MIA adversaries. Empirical evaluations across diverse datasets and GNN architectures confirm that DPS provides robust protection against inference attacks with minimal compromise to node classification accuracy.