Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

As the right to be forgotten has been legislated worldwide, many studies attempt to design
unlearning mechanisms to protect users' privacy when they want to leave machine learning …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …

Graph unlearning, which involves deleting graph elements such as nodes, node labels, and
relationships from a trained graph neural network (GNN) model, is crucial for real-world …

As the right to be forgotten has been legislated worldwide, many studies attempt to design
machine unlearning mechanisms to enable data erasure from a trained model. Existing …

There has been a growing interest in Machine Unlearning recently, primarily due to legal
requirements such as the General Data Protection Regulation (GDPR) and the California …

Recent data-privacy laws have sparked interest in machine unlearning, which involves
removing the effect of specific training samples from a learnt model as if they were never …

As the use of machine learning (ML) models is becoming increasingly popular in many real-
world applications, there are practical challenges that need to be addressed for model …