Graph neural network (GNN) is a powerful tool for analyzing graph-structured data. However, their vulnerability to adversarial attacks raises serious concerns, especially when dealing with sensitive information. Local Differential Privacy (LDP) offers a privacy-preserving framework for training GNNs, but its impact on adversarial robustness remains underexplored. This paper investigates adversarial attacks on LDP-protected GNNs. We explore how the privacy guarantees of LDP can be leveraged or hindered by adversarial perturbations. The effectiveness of existing attack methods on LDP-protected GNNs are analyzed and potential challenges in crafting adversarial examples under LDP constraints are discussed. Additionally, we suggest directions for defending LDP-protected GNNs against adversarial attacks. This work investigates the interplay between privacy and security in graph learning, highlighting the need for robust and privacy-preserving GNN architectures.

翻译：图神经网络（GNN）是分析图结构数据的强大工具。然而，其对对抗攻击的脆弱性引发了严重关切，尤其是在处理敏感信息时。本地差分隐私（LDP）为训练GNN提供了一种隐私保护框架，但其对对抗鲁棒性的影响仍未被充分探索。本文研究了针对LDP保护GNN的对抗攻击。我们探讨了LDP的隐私保证如何被对抗性扰动利用或削弱。分析了现有攻击方法对LDP保护GNN的有效性，并讨论了在LDP约束下构造对抗样本的潜在挑战。此外，我们提出了防御LDP保护GNN免受对抗攻击的方向。这项工作研究了图学习中隐私与安全性之间的相互作用，强调了构建鲁棒且隐私保护的GNN架构的必要性。