Adversarial Attacks on Locally Private Graph Neural Networks

• URL: http://arxiv.org/abs/2603.20746v1
• Date: Sat, 21 Mar 2026 10:23:48 GMT
• Title: Adversarial Attacks on Locally Private Graph Neural Networks
• Abstract summary: Local Differential Privacy (LDP) offers a privacy-preserving framework for training GNNs.<n>This paper investigates adversarial attacks on LDP-protected GNNs.
• Score: 6.7726810135041235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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