Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

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

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 increasing data privacy concerns in recommendation systems have made federated
recommendations attract more and more attention. Existing federated recommendation …

Over the past decades, the abundance of personal data has led to the rapid development of
machine learning models and important advances in artificial intelligence (AI). However …

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 …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

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 …