Machine unlearning is the process through which a deployed machine learning model is
made to forget about some of its training data points. While naively retraining the model from …

Once users have shared their data online, it is generally difficult for them to revoke access
and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because …

Machine unlearning, ie having a model forget about some of its training data, has become
increasingly more important as privacy legislation promotes variants of the right-to-be …

Recent legislation has led to interest in machine unlearning, ie, removing specific training
samples from a predictive model as if they never existed in the training dataset. Unlearning …

Deep machine unlearning is the problem of'removing'from a trained neural network a subset
of its training set. This problem is very timely and has many applications, including the key …

Unlearning the data observed during the training of a machine learning (ML) model is an
important task that can play a pivotal role in fortifying the privacy and security of ML-based …

The right to be forgotten requires the removal or" unlearning" of a user's data from machine
learning models. However, in the context of Machine Learning as a Service (MLaaS) …

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 …

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 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 …