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A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges
Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …
domains, such as social network analysis, biochemistry, financial fraud detection, and …
Privacy-preserving explainable AI: a survey
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
Privacy leakage on dnns: A survey of model inversion attacks and defenses
Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional
performance across numerous applications. However, Model Inversion (MI) attacks, which …
performance across numerous applications. However, Model Inversion (MI) attacks, which …
A survey of privacy-preserving model explanations: Privacy risks, attacks, and countermeasures
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
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 …
Towards a game-theoretic understanding of explanation-based membership inference attacks
Abstract Model explanations improve the transparency of black-box machine learning (ML)
models and their decisions; however, they can also enable privacy threats like membership …
models and their decisions; however, they can also enable privacy threats like membership …
Does black-box attribute inference attacks on graph neural networks constitute privacy risk?
Graph neural networks (GNNs) have shown promising results on real-life datasets and
applications, including healthcare, finance, and education. However, recent studies have …
applications, including healthcare, finance, and education. However, recent studies have …
Model Inversion Attacks: A Survey of Approaches and Countermeasures
The success of deep neural networks has driven numerous research studies and
applications from Euclidean to non-Euclidean data. However, there are increasing concerns …
applications from Euclidean to non-Euclidean data. However, there are increasing concerns …
Privacy and transparency in graph machine learning: A unified perspective
Graph Machine Learning (GraphML), whereby classical machine learning is generalized to
irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of …
irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of …
Edge private graph neural networks with singular value perturbation
Graph neural networks (GNNs) play a key role in learning representations from graph-
structured data and are demonstrated to be useful in many applications. However, the GNN …
structured data and are demonstrated to be useful in many applications. However, the GNN …