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 …

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 …

The explosive growth of machine learning has made it a critical infrastructure in the era of
artificial intelligence. The extensive use of data poses a significant threat to individual …

This review explores machine unlearning (MUL) in recommendation systems, addressing
adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL …

The" right-to-be-forgotten" requires the removal of personal data from trained machine
learning (ML) models with machine unlearning. Conducting such unlearning with low …

In response to legislation mandating companies to honor the\textit {right to be forgotten} by
erasing user data, it has become imperative to enable data removal in Vertical Federated …

Random Forests are widely recognized for establishing efficacy in classification and
regression tasks, standing out in various domains such as medical diagnosis, finance, and …

In response to legislation, companies are now mandated to honor the right to be forgotten by
erasing user data. Consequently, it has become imperative to enable data removal in …

In today's data-rich era, large models continuously consume vast troves of personal data,
raising pertinent questions about user consent and its implications in training machine …