A semi-supervised privacy-preserving graph classification framework enhanced by graph contrastive learning

While Graph Neural Networks (GNNs) have demonstrated impressive performance across various domains, their high interconnectivity makes them inherently vulnerable to privacy leakage. Traditional Differential Privacy (DP) methods—such as Differentially Private Stochastic Gradient Descent (DPSGD) and Private Aggregation of Teacher Ensembles (PATE)—either fail to provide sufficient protection in graph settings or require impractical assumptions, such as strictly disjoint data partitions and large volumes of labeled data. To address these challenges, we propose a semi-supervised privacy-preserving graph classification framework that integrates the differentially private k-nearest neighbor (DP-kNN) mechanism with contrastive pre-training. Unlike prior approaches, We design a DP-kNN mechanism that securely transfers private labels to public graphs by aggregating and perturbing nearest-neighbor labels, without requiring multiple teacher models or strictly disjoint data splits. To further improve the quality and robustness of the pseudo-labels generated by DP-kNN, we introduce a graph contrastive learning pre-training stage, which establishes a noise-resistant feature foundation for the graph data, thereby mitigating the impact of differential privacy noise during the label transfer process. Finally, pseudo-labeled public graphs are used for fine-tuning, yielding high utility with strong privacy guarantees. We further conduct a rigorous privacy analysis under the Rényi Differential Privacy (RDP) framework, incorporating privacy amplification via subsampling. Extensive experiments on eight benchmark graph datasets demonstrate that our approach achieves competitive classification performance under strict privacy budgets, offering a scalable and practical solution for privacy-aware graph learning.