On the Interaction between Node Fairness and Edge Privacy in Graph Neural Networks
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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.
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