Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

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 …

With the growing demand for the right to be forgotten, there is an increasing need for
machine learning models to forget sensitive data and its impact. To address this, the …

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

Abstract The Right to be Forgotten is a core principle outlined by regulatory frameworks such
as the EU's General Data Protection Regulation (GDPR). This principle allows individuals to …

Given the availability of abundant data, deep learning models have been advanced and
become ubiquitous in the past decade. In practice, due to many different reasons (eg …

Over the past years, Machine Learning-as-a-Service (MLaaS) has received a surging
demand for supporting Machine Learning-driven services to offer revolutionized user …

We study the problem of $(\epsilon,\delta) $-certified machine unlearning for minimax
models. Most of the existing works focus on unlearning from standard statistical learning …

Graph unlearning emerges as a crucial advancement in the pursuit of responsible AI,
providing the means to remove sensitive data traces from trained models, thereby upholding …