Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)

TL;DR

This study evaluates privacy risks in graph neural network representations by demonstrating that higher-order simplicial neural network outputs are more vulnerable to graph reconstruction attacks, highlighting the need for more privacy-preserving methods.

Contribution

It introduces a graph reconstruction attack method and compares privacy leakage across GCN, GAT, and SNN representations, revealing higher vulnerability in SNN outputs.

Findings

SNN outputs are most susceptible to privacy leakage.

GRA effectiveness varies with representation type.

Higher-order features in SNN reduce privacy protection.

Abstract

In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA). We propose a GRA that recovers a graph's adjacency matrix from the representations via a graph decoder that minimizes the reconstruction loss between the partial graph and the reconstructed graph. We study three types of representations that are trained on the graph, i.e., representations output from graph convolutional network (GCN), graph attention network (GAT), and our proposed simplicial neural network (SNN) via a higher-order combinatorial Laplacian. Unlike the first two types of representations that only encode pairwise relationships, the third type of representation, i.e., SNN outputs, encodes higher-order interactions (e.g., homological features) between nodes. We find that the SNN outputs reveal the lowest privacy-preserving ability to defend the GRA, followed by those of GATs and GCNs, which indicates the importance of building more private representations with higher-order node information that could defend the potential threats, such as GRAs.