As some recent information security legislation endowed users with unconditional rights to
be forgotten by any trained machine learning model, personalised IoT service providers …

Recently users' right-to-be-forgotten is stipulated by many laws and regulations. However,
only removing the data from the dataset is not enough, as machine learning models would …

The emergence of the right to be forgotten has sparked interest in federated unlearning.
Researchers utilize federated unlearning to address the issue of removing user …

As the right to be forgotten has been legislated worldwide, many studies attempt to design
unlearning mechanisms to protect users' privacy when they want to leave machine learning …

Once users have shared their data online, it is generally difficult for them to revoke access
and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because …

Owing to its practical configuration to edge computing and privacy preservation capabilities,
federated learning (FL) has been increasingly appealing in Internet of Things (IoT) networks …

Over the past few years, Machine Learning-as-a-Service (MLaaS) has received a surging
demand for supporting Machine Learning-driven services to offer revolutionized user …

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 …

The Right to be Forgotten gives a data owner the right to revoke their data from an entity
storing it. In the context of federated learning, the Right to be Forgotten requires that, in …

Recently, the enactment of privacy regulations has promoted the rise of the machine
unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise …