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 …

Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the “right to be …

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

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 …

The marriage of federated learning and recommender system (FedRec) has been widely
used to address the growing data privacy concerns in personalized recommendation …

Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

Federated Recommender Systems (FedRecs) are considered privacy-preserving
techniques to collaboratively learn a recommendation model without sharing user data …

In recent years, recommender systems are crucially important for the delivery of
personalized services that satisfy users' preferences. With personalized recommendation …

Conclusion In this letter, we propose E2URec, the efficient and effective unlearning method
for LLMRec. Our method enables LLMRec to efficiently forget the specific data by only …

Recommendation systems play a crucial role in providing web-based suggestion utilities by
leveraging user behavior, preferences, and interests. In the context of privacy concerns and …