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：图神经网络（GNN）因其在各类图推理任务中学习节点嵌入的卓越能力而日益受到关注，但训练过程可能引发隐私问题。为此，我们提出在去中心化节点上采用链路本地差分隐私，使节点能够与不可信服务器协作训练GNN，同时不泄露任何链路的存在性。本方法将隐私预算分别分配至图的链路和度数，使服务器能通过贝叶斯估计更好地去噪图拓扑结构，从而缓解LDP对训练后GNN准确率的负面影响。我们限定了推断链路概率相对于真实图拓扑的均值绝对误差。随后针对不同隐私设置提出两种互补的LDP机制变体：其中一种在较低隐私预算下估算更少的链路，以避免高不确定性时的假阳性链路估计；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，我们提出融合两种策略的混合变体，能在不同隐私预算下取得更优性能。大量实验表明，本方法在变隐私预算下的准确率优于现有方法。