Graph neural networks (GNNs) have become instrumental in diverse real-world
applications, offering powerful graph learning capabilities for tasks such as
social networks and medical data analysis. Despite their successes, GNNs are
vulnerable to adversarial attacks, including membership inference attacks
(MIA), which threaten privacy by identifying whether a record was part of the
model's training data. While existing research has explored MIA in GNNs under
graph inductive learning settings, the more common and challenging graph
transductive learning setting remains understudied in this context. This paper
addresses this gap and proposes an effective two-stage defense, Graph
Transductive Defense (GTD), tailored to graph transductive learning
characteristics. The gist of our approach is a combination of a train-test
alternate training schedule and flattening strategy, which successfully reduces
the difference between the training and testing loss distributions. Extensive
empirical results demonstrate the superior performance of our method (a
decrease in attack AUROC by $9.42\%$ and an increase in utility performance by
$18.08\%$ on average compared to LBP), highlighting its potential for seamless
integration into various classification models with minimal overhead.

本文解决了图转导学习中成员推理攻击的问题，提出了一种有效的两阶段防御方法 Graph Transductive Defense（GTD），该方法通过训练 - 测试交替训练计划和压缩策略的组合成功减少了训练和测试损失分布之间的差异，实验证明其性能优于 LBP（攻击 AUROC 平均下降 9.42%，实用性能平均提高 18.08%），可无缝集成到各种分类模型中，且开销较小。