Due to the emergence of graph neural networks (GNNs) and their widespread implementation in real-world scenarios, the fairness and privacy of GNNs have attracted considerable interest since they are two essential social concerns in the era of building trustworthy GNNs. Existing studies have respectively explored the fairness and privacy of GNNs and exhibited that both fairness and privacy are at the cost of GNN performance. However, the interaction between them is yet to be explored and understood. In this paper, we investigate the interaction between the fairness of a GNN and its privacy for the first time. We empirically identify that edge privacy risks increase when the individual fairness of nodes is improved. Next, we present the intuition behind such a trade-off and employ the influence function and Pearson correlation to measure it theoretically. To take the performance, fairness, and privacy of GNNs into account simultaneously, we propose implementing fairness-aware reweighting and privacy-aware graph structure perturbation modules in a retraining mechanism. Experimental results demonstrate that our method is effective in implementing GNN fairness with limited performance cost and restricted privacy risks.

翻译：随着图神经网络（GNNs）的出现及其在现实场景中的广泛应用，GNN的公平性与隐私性作为构建可信GNN时代的两大关键社会关切，已引起学术界的高度关注。现有研究分别探讨了GNN的公平性与隐私性，并表明两者均以牺牲GNN性能为代价。然而，二者之间的相互作用仍有待探索与理解。本文首次研究了GNN公平性与其隐私性之间的相互作用。通过实证分析，我们识别出当节点的个体公平性提升时，边隐私风险随之增加。随后，我们阐释了这种权衡背后的直觉，并利用影响函数与皮尔逊相关系数对其进行理论度量。为同时兼顾GNN的性能、公平性与隐私性，我们提出在重训练机制中集成公平感知重加权模块与隐私感知图结构扰动模块。实验结果表明，该方法能在有限性能损失与受控隐私风险下有效实现GNN的公平性。