In this paper, we present a stealthy and effective attack that exposes privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private links within graph-structured data. Focusing on the inductive setting where new nodes join the graph and an API is used to query predictions, we investigate the potential leakage of private edge information. We also propose methods to preserve privacy while maintaining model utility. Our attack demonstrates superior performance in inferring the links compared to the state of the art. Furthermore, we examine the application of differential privacy (DP) mechanisms to mitigate the impact of our proposed attack, we analyze the trade-off between privacy preservation and model utility. Our work highlights the privacy vulnerabilities inherent in GNNs, underscoring the importance of developing robust privacy-preserving mechanisms for their application.

翻译：在本文中，我们提出了一种隐蔽且高效的攻击方法，通过推断图结构数据中的私有链路来揭示图神经网络（GNNs）中的隐私漏洞。针对新节点加入图并利用API查询预测的归纳式设置，我们研究了私有边信息的潜在泄露风险，并提出了在保持模型效用的同时保护隐私的方法。我们的攻击在链路推断方面相比现有技术表现出更优性能。此外，我们探究了应用差分隐私（DP）机制来缓解所提攻击影响的效果，分析了隐私保护与模型效用之间的权衡。本研究突显了GNNs固有的隐私漏洞，强调为其应用开发稳健的隐私保护机制的重要性。