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

Good data stewardship requires removal of data at the request of the data's owner. This
raises the question if and how a trained machine-learning model, which implicitly stores …

Responding to user data deletion requests, removing noisy examples, or deleting corrupted
training data are just a few reasons for wanting to delete instances from a machine learning …

Recommender systems provide essential web services by learning users' personal
preferences from collected data. However, in many cases, systems also need to forget some …

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 …

Recent legislation of the" right to be forgotten" has led to the interest in machine unlearning,
where the learned models are endowed with the function to forget information about specific …

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 …

Preserving the performance of a trained model while removing unique characteristics of
marked training data points is challenging. Recent research usually suggests retraining a …

Software systems that learn from user data with machine learning (ML) have become
ubiquitous over the last years. Recent law such as the" General Data Protection …

This paper aims to develop a novel cost-effective framework for face identification, which
progressively maintains a batch of classifiers with the increasing face images of different …