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 …

Abstract Machine learning models are not static and may need to be retrained on slightly
changed datasets, for instance, with the addition or deletion of a set of data points. This has …

Modern privacy regulations grant citizens the right to be forgotten by products, services and
companies. In case of machine learning (ML) applications, this necessitates deletion of data …

Abstract Machine unlearning has become an important area of research due to an
increasing need for machine learning (ML) applications to comply with the emerging data …

We show that the influence of a subset of the training samples can be removed--or"
forgotten"--from the weights of a network trained on large-scale image classification tasks …

We describe a procedure for removing dependency on a cohort of training data from a
trained deep network that improves upon and generalizes previous methods to different …

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

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 …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …

Streaming algorithms are generally judged by the quality of their solution, memory footprint,
and computational complexity. In this paper, we study the problem of maximizing a …