On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

TL;DR

This paper investigates privacy risks in GNNs by modeling them as Markov chains, proposing attack and defense mechanisms guided by information theory, and demonstrating state-of-the-art results on multiple datasets.

Contribution

It introduces a novel Markov chain approximation framework for graph reconstruction attacks and defenses, providing systematic insights and practical methods.

Findings

Achieves state-of-the-art attack success rates on six datasets.

Proposes a chain-based defense method that reduces attack fidelity with moderate accuracy loss.

Demonstrates the effectiveness of information theory-guided mechanisms in GNN privacy protection.

Abstract

Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored. To close this gap, we perform the first comprehensive study of graph reconstruction attack that aims to reconstruct the adjacency of nodes. We show that a range of factors in GNNs can lead to the surprising leakage of private links. Especially by taking GNNs as a Markov chain and attacking GNNs via a flexible chain approximation, we systematically explore the underneath principles of graph reconstruction attack, and propose two information theory-guided mechanisms: (1) the chain-based attack method with adaptive designs for extracting more private information; (2) the chain-based defense method that sharply reduces the attack fidelity with moderate accuracy loss. Such two objectives disclose a critical belief that to recover better in attack, you must extract more multi-aspect knowledge from the trained GNN; while to learn safer for defense, you must forget more link-sensitive information in training GNNs. Empirically, we achieve state-of-the-art results on six datasets and three common GNNs. The code is publicly available at: https://github.com/tmlr-group/MC-GRA.