Removing the influence of a specified subset of training data from a machine learning model
may be required to address issues such as privacy, fairness, and data quality. Retraining the …

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

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 …

With the introduction of data protection and privacy regulations, it has become crucial to
remove the lineage of data on demand from a machine learning (ML) model. In the last few …

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

Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …

Machine unlearning is the problem of removing the effect of a subset of training data
(the''forget set'') from a trained model without damaging the model's utility eg to comply with …

Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring
privacy in modern artificial intelligence models. This technique primarily aims to eradicate …

Machine unlearning has become a promising solution for fulfilling the" right to be forgotten",
under which individuals can request the deletion of their data from machine learning …

The rise of the phenomenon of the “right to be forgotten” has prompted research on machine
unlearning, which grants data owners the right to actively withdraw data that has been used …