We initiate an empirical investigation into differentially private graph neural networks on population graphs from the medical domain by examining privacy-utility trade-offs at different privacy levels on both real-world and synthetic datasets and performing auditing through membership inference attacks. Our findings highlight the potential and the challenges of this specific DP application area. Moreover, we find evidence that the underlying graph structure constitutes a potential factor for larger performance gaps by showing a correlation between the degree of graph homophily and the accuracy of the trained model.

翻译：我们针对医疗领域群体图上的差分隐私图神经网络开展了实证研究，通过考察不同隐私水平下真实与合成数据集上的隐私-效用权衡，并利用成员推断攻击进行审计。研究结果揭示了这一特定差分隐私应用领域的潜力与挑战。此外，我们发现基础图结构是造成模型性能差距的关键因素——图同质性程度与训练模型准确率之间存在相关性，这为上述论断提供了实证支持。