Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …

Machine learning models are shown to face a severe threat from Model Extraction Attacks,
where a well-trained private model owned by a service provider can be stolen by an attacker …

Graph Neural Networks (GNNs) have achieved promising performance in various real-world
applications. However, recent studies have shown that GNNs are vulnerable to adversarial …

Graph Neural Networks (GNNs), which generalize traditional deep neural networks on
graph data, have achieved state-of-the-art performance on several graph analytical tasks …

Node injection attack on Graph Neural Networks (GNNs) is an emerging and practical attack
scenario that the attacker injects malicious nodes rather than modifying original nodes or …

Many real-world data comes in the form of graphs, such as social networks and protein
structure. To fully utilize the information contained in graph data, a new family of machine …

Graph is an important data representation ubiquitously existing in the real world. However,
analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph …

Backdoor attacks represent a serious threat to neural network models. A backdoored model
will misclassify the trigger-embedded inputs into an attacker-chosen target label while …

Abstract Graph Neural Networks (GNNs) have drawn significant attentions over the years
and been broadly applied to essential applications requiring solid robustness or vigorous …

Recent research has shown that machine learning (ML) models are vulnerable to privacy
attacks that leak information about the training data. In this work, we consider Graph Neural …