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 …

Graph unlearning emerges as a crucial advancement in the pursuit of responsible AI,
providing the means to remove sensitive data traces from trained models, thereby upholding …

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 …

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 …

With the implementation of personal data privacy regulations, the field of machine learning
(ML) faces the challenge of the" right to be forgotten". Machine unlearning has emerged to …

The rapid expansion of Internet users results in an immense influx of network traffic within
extensive cloud data centers. Accurate and instantaneous identification and forecasting of …

Despite the booming market of the Internet of Things (IoT), the weak security protection of
IoT devices makes anomaly detection in IoT systems extremely challenging. This …

In smart homes, as a hallmark application of the Internet of Things (IoT), home automation
platforms manage IoT devices via automation rules deployed by users, facilitating complex …

The field of intelligent detection is a very potential research direction. Deep learning
technology can collect vehicle flow data, detection signals and pedestrian location …

This extensive research uses state-of-the-art machine learning methods to explore the
complex domain of digital banking real-time fraud detection. The goal is to drastically reduce …