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Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks by Niu, Peizhi; Pan, Chao; Chen, Siheng; Milenkovic, Olgica is a scholarly article available to read on EtoBox.

What is Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks about?

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

Author

Niu, Peizhi; Pan, Chao; Chen, Siheng; Milenkovic, Olgica

Published

2024

Language

EN