Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information. However, the existing methods ignore that nodes with different importance may yield diverse privacy demands, which may lead to over-protect some nodes and decrease model utility. In this paper, we study the problem of importance-grained privacy, where nodes contain personal data that need to be kept private but are critical for training a GNN. We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information. First, we propose a Topology-based Node Importance Estimation (TNIE) method to infer unknown node importance with neighborhood and centrality awareness. Second, an adaptive private aggregation method is proposed to perturb neighborhood aggregation from node-importance-grain. Third, we propose to privately train a graph learning algorithm on perturbed aggregations in adaptive residual connection mode over multi-layers convolution for node-wise tasks. Theoretically analysis shows that NAP-GNN satisfies privacy guarantees. Empirical experiments over real-world graph datasets show that NAP-GNN achieves a better trade-off between privacy and accuracy.

翻译：图神经网络（GNN）结合差分隐私已被提出用于在节点包含个人敏感信息时保护图隐私。然而，现有方法忽略了不同重要性的节点可能具有不同的隐私需求，这可能导致对某些节点的过度保护并降低模型效用。本文研究了重要性粒度隐私问题，其中节点包含需要保密但同时对训练GNN至关重要的个人数据。我们提出NAP-GNN——一种基于节点重要性粒度的隐私保护GNN算法，该算法通过自适应差分隐私提供隐私保证以保护节点信息。首先，我们提出基于拓扑的节点重要性估计（TNIE）方法，通过邻域和中心性感知推断未知节点重要性；其次，提出自适应隐私聚合方法，从节点重要性粒度扰动邻域聚合；第三，提出在多层卷积的自适应残差连接模式下，对扰动聚合进行私有图学习训练以执行节点级任务。理论分析表明NAP-GNN满足隐私保证。在真实图数据集上的实验表明，NAP-GNN实现了隐私与准确率之间更好的权衡。