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 …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

The rapid advancement of Large Language Models (LLMs) has demonstrated their vast
potential across various domains, attributed to their extensive pretraining knowledge and …

Generative AI technologies have been deployed in many places, such as (multimodal) large
language models and vision generative models. Their remarkable performance should be …

Although graph neural networks have exhibited remarkable performance in various graph
tasks, a significant concern is their vulnerability to adversarial attacks. Consequently, many …

Relational learning has gained significant attention, led by the expressiveness of Graph
Neural Networks (GNNs) on graph data. While the inherent biases in common graph data …

Collaborative filtering (CF) is a prevalent technique utilized in recommender systems (RSs),
and has been extensively deployed in various real-world applications. A recent study in CF …

AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …

This study investigates the concept of theright to be forgotten'within the context of large
language models (LLMs). We explore machine unlearning as a pivotal solution, with a focus …

Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but
also pose risks by learning and generating copyrighted material, leading to significant legal …