A comprehensive review on deep learning algorithms: Security and privacy issues
Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …
various complicated tasks that begin to modify and improve with experiences. It has become …
Hierarchical graph transformer with adaptive node sampling
The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …
including natural language processing and computer vision. However, when it comes to …
Molecule generation for target protein binding with structural motifs
Designing ligand molecules that bind to specific protein binding sites is a fundamental
problem in structure-based drug design. Although deep generative models and geometric …
problem in structure-based drug design. Although deep generative models and geometric …
Backdoor defense via deconfounded representation learning
Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks,
where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …
where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …
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 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 …
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 …
[HTML][HTML] Workplace security and privacy implications in the GenAI age: A survey
Abstract Generative Artificial Intelligence (GenAI) is transforming the workplace, but its
adoption introduces significant risks to data security and privacy. Recent incidents …
adoption introduces significant risks to data security and privacy. Recent incidents …
An equivariant generative framework for molecular graph-structure co-design
Designing molecules with desirable physiochemical properties and functionalities is a long-
standing challenge in chemistry, material science, and drug discovery. Recently, machine …
standing challenge in chemistry, material science, and drug discovery. Recently, machine …
Mibench: A comprehensive benchmark for model inversion attack and defense
Model Inversion (MI) attacks aim at leveraging the output information of target models to
reconstruct privacy-sensitive training data, raising widespread concerns on privacy threats of …
reconstruct privacy-sensitive training data, raising widespread concerns on privacy threats of …