Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

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 …

Deploying machine learning models in production may allow adversaries to infer sensitive
information about training data. There is a vast literature analyzing different types of …

In light of the wide application of Graph Neural Networks (GNNs), Membership Inference
Attack (MIA) against GNNs raises severe privacy concerns, where training data can be …

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with
Differential Privacy (DP). We propose a novel differentially private GNN based on …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional
performance across numerous applications. However, Model Inversion (MI) attacks, which …

Property inference attacks allow an adversary to extract global properties of the training
dataset from a machine learning model. Such attacks have privacy implications for data …