Federated unlearning has emerged very recently as an attempt to realize “the right to be
forgotten” in the context of federated learning. While the current literature is making efforts on …

Machine learning models exhibit two seemingly contradictory phenomena: training data
memorization, and various forms of forgetting. In memorization, models overfit specific …

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 …

As industrial applications are increasingly automated by machine learning models,
enforcing personal data ownership and intellectual property rights requires tracing training …

摘要机器学习被广泛应用于各个领域, 已成为推动各行业革命的强大动力,
极大促进了人工智能的繁荣与发展. 同时, 机器学习模型的训练和预测均需要大量数据 …

Member Inference Attack (MIA) is a key measure for evaluating privacy leakage in Machine
Learning (ML) models, aiming to distinguish private members from non-members by training …

Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a
data-constrained setting. In current meta-learning methods, task learners locally learn …

Machine learning models have been shown to leak sensitive information about their training
datasets. Models are increasingly deployed on devices, raising concerns that white-box …