Demystifying uneven vulnerability of link stealing attacks against graph neural networks
While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …
real-world applications, they have been shown to be vulnerable to a growing number of …
Node-level membership inference attacks against graph neural networks
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 …
structure. To fully utilize the information contained in graph data, a new family of machine …
Group property inference attacks against graph neural networks
X Wang, WH Wang - Proceedings of the 2022 ACM SIGSAC Conference …, 2022 - dl.acm.org
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 …
attacks that leak information about the training data. In this work, we consider Graph Neural …
A survey on privacy in graph neural networks: Attacks, preservation, and applications
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …
handle graph-structured data and the improvement in practical applications. However, many …
A unified framework of graph information bottleneck for robustness and membership privacy
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured
data. However, recent works show that GNNs are vulnerable to adversarial attacks which …
data. However, recent works show that GNNs are vulnerable to adversarial attacks which …
Learning privacy-preserving graph convolutional network with partially observed sensitive attributes
Recent studies have shown Graph Neural Networks (GNNs) are extremely vulnerable to
attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs …
attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs …
Netfense: Adversarial defenses against privacy attacks on neural networks for graph data
IC Hsieh, CT Li - IEEE Transactions on Knowledge and Data …, 2021 - ieeexplore.ieee.org
Recent advances in protecting node privacy on graph data and attacking graph neural
networks (GNNs) gain much attention. The eye does not bring these two essential tasks …
networks (GNNs) gain much attention. The eye does not bring these two essential tasks …
Adapting membership inference attacks to GNN for graph classification: Approaches and implications
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 …
Attack (MIA) against GNNs raises severe privacy concerns, where training data can be …
Model inversion attacks against graph neural networks
Many data mining tasks rely on graphs to model relational structures among individuals
(nodes). Since relational data are often sensitive, there is an urgent need to evaluate the …
(nodes). Since relational data are often sensitive, there is an urgent need to evaluate the …
On strengthening and defending graph reconstruction attack with markov chain approximation
Although powerful graph neural networks (GNNs) have boosted numerous real-world
applications, the potential privacy risk is still underexplored. To close this gap, we perform …
applications, the potential privacy risk is still underexplored. To close this gap, we perform …