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 …

With the growing demand for the right to be forgotten, there is an increasing need for
machine learning models to forget sensitive data and its impact. To address this, the …

Given the availability of abundant data, deep learning models have been advanced and
become ubiquitous in the past decade. In practice, due to many different reasons (eg …

The evolving paradigm of Large Language Model-based Recom-mendation (LLMRec)
customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) …

Content sharing, whether in Online Social Networks (OSNs) or even in the Internet of Things
(IoT), serves as a pivotal link in the flow of data. To better protect the privacy of shared …

Abstract Machine unlearning is a cutting‐edge technology that embodies the privacy legal
principle of the right to be forgotten within the realm of machine learning (ML). It aims to …

In education data mining (EDM) communities, machine learning has achieved remarkable
success in discovering patterns and structures to tackle educational challenges. Notably …

The right to be forgotten mandates that machine learning models enable the erasure of a
data owner's data and information from a trained model. Removing data from the dataset …

The past few years have seen an intense research interest in the practical needs of the" right
to be forgotten", which enables machine learning models to unlearn a fraction of training …