Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using link local differential privacy over decentralized nodes, enabling collaboration with an untrusted server to train GNNs without revealing the existence of any link. Our approach spends the privacy budget separately on links and degrees of the graph for the server to better denoise the graph topology using Bayesian estimation, alleviating the negative impact of LDP on the accuracy of the trained GNNs. We bound the mean absolute error of the inferred link probabilities against the ground truth graph topology. We then propose two variants of our LDP mechanism complementing each other in different privacy settings, one of which estimates fewer links under lower privacy budgets to avoid false positive link estimates when the uncertainty is high, while the other utilizes more information and performs better given relatively higher privacy budgets. Furthermore, we propose a hybrid variant that combines both strategies and is able to perform better across different privacy budgets. Extensive experiments show that our approach outperforms existing methods in terms of accuracy under varying privacy budgets.

翻译：摘要：图神经网络因其在学习节点嵌入以完成各类图推理任务中的卓越能力而日益受到关注，但其训练过程可能引发隐私问题。为解决这一问题，我们提出在去中心化节点上采用链路本地差分隐私，使得节点能够与不可信服务器协作训练图神经网络，同时不暴露任何链路的存在性。我们的方法将隐私预算分别分配至图的链路和度数，使服务器能够通过贝叶斯估计更有效地对图拓扑进行去噪，从而缓解本地差分隐私对训练所得图神经网络准确性的负面影响。我们对推断链路概率相对于真实图拓扑的平均绝对误差进行了界定。随后，我们提出了两种互补的本地差分隐私机制变体，适用于不同的隐私设置：其中一种在较低隐私预算下估计更少的链路，以避免在高不确定性时产生假阳性链路估计；另一种则利用更多信息，在相对较高的隐私预算下表现更优。此外，我们提出了一种混合变体，该变体结合了两种策略，能够在不同隐私预算下实现更优性能。大量实验表明，在变化的隐私预算下，我们的方法在准确性方面优于现有方法。