Multi-Access Edge Computing (MEC) attracts much attention from the scientific community
due to its scientific, technical, and commercial implications. In particular, the European …

Federated learning can combine a large number of scattered user groups and train models
collaboratively without uploading data sets, so as to avoid the server collecting user …

Deep learning models are often trained on datasets that contain sensitive information such
as individuals' shopping transactions, personal contacts, and medical records. An …

Motivated by the security risks of deep neural networks, such as various membership and
attribute inference attacks, differential privacy has emerged as a promising approach for …

Gradient leakage attacks are considered one of the wickedest privacy threats in deep
learning as attackers covertly spy gradient updates during iterative training without …

Nowadays, differential privacy (DP) has become a well-accepted standard for privacy
protection, and deep neural networks (DNN) have been immensely successful in machine …

With the advent of the era of big data, deep learning has become a prevalent building block
in a variety of machine learning or data mining tasks, such as signal processing, network …

Deep learning holds a great promise of revolutionizing healthcare and medicine.
Unfortunately, various inference attack models demonstrated that deep learning puts …

Deep learning (DL) has been adopted in a broad range of Internet-of-Things (IoT)
applications such as auto-driving, intelligent healthcare and smart grids, but limitations such …

The modern technological advancement influences the growth of the cyber–physical system
and cyber–social system to a more advanced computing system cyber–physical–social …