Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …

The practical needs of the" right to be forgotten" and poisoned data removal call for efficient
machine unlearning techniques, which enable machine learning models to unlearn, or to …

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 …

In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML)
model to infer and reconstruct private training data. Remarkable progress has been made in …

Federated learning (FL) has achieved state-of-the-art performance in distributed learning
tasks with privacy requirements. However, it has been discovered that FL is vulnerable to …

A traditional centralized method of training AI models has been put to the test by the
emergence of data stores and public privacy concerns. To overcome these issues, the …

In this survey, we provide a comprehensive review of more than 200 articles, technical
reports, and GitHub repositories published over the last 10 years on the recent …

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

High security for physical items such as intelligent machinery and residential appliances is
provided via the Internet of Things (IoT). The physical objects are given a distinct online …