Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications. To build trustworthy GNNs, two aspects - fairness and privacy - have emerged as critical considerations. Previous studies have separately examined the fairness and privacy aspects of GNNs, revealing their trade-off with GNN performance. Yet, the interplay between these two aspects remains unexplored. In this paper, we pioneer the exploration of the interaction between the privacy risks of edge leakage and the individual fairness of a GNN. Our theoretical analysis unravels that edge privacy risks unfortunately escalate when the nodes' individual fairness improves. Such an issue hinders the accomplishment of privacy and fairness of GNNs at the same time. To balance fairness and privacy, we carefully introduce fairness-aware loss reweighting based on influence function and privacy-aware graph structure perturbation modules within a fine-tuning mechanism. Experimental results underscore the effectiveness of our approach in achieving GNN fairness with limited performance compromise and controlled privacy risks. This work contributes to the comprehensively developing trustworthy GNNs by simultaneously addressing both fairness and privacy aspects.

翻译：图神经网络（GNNs）因其广泛的现实应用而备受关注。为构建可信的GNNs，公平性与隐私性已成为关键考量因素。先前研究分别探讨了GNNs的公平性与隐私性，揭示了其与GNN性能之间的权衡关系，但这两个方面之间的相互作用仍未被探索。本文率先研究了边泄露的隐私风险与GNN个体公平性之间的交互作用。理论分析表明，当节点的个体公平性提升时，边隐私风险不幸加剧。这一问题阻碍了GNNs同时实现隐私性与公平性。为平衡公平性与隐私性，我们巧妙地在微调机制中引入了基于影响函数的公平感知损失重加权模块与隐私感知图结构扰动模块。实验结果证明了该方法在实现GNN公平性的同时，能够以有限性能损失和可控隐私风险取得成效。本工作通过同时兼顾公平性与隐私性，为全面开发可信GNNs做出了贡献。