Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …

Data privacy and security have become a non-negligible factor in load forecasting. Previous
researches mainly focus on training stage enhancement. However, once the model is …

Machine Unlearning (MU) is to forget data from a well-trained model, which is practically
important due to the" right to be forgotten". In this paper, we start from the fundamental …

Recent advancements in Machine Unlearning (MU) have introduced solutions to selectively
remove certain training samples, such as those with outdated or sensitive information, from …

The advent of Federated Learning (FL) highlights the practical necessity for the'right to be
forgotten'for all clients, allowing them to request data deletion from the machine learning …

Machine unlearning (MU) aims to erase data from a model as if it never saw them during
training. To this extent, existing MU approaches assume complete or partial access to the …

Machine Unlearning removes specific knowledge about training data samples from an
already trained model. It has significant practical benefits, such as purging private …