Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Recently emerged federated learning (FL) is an attractive distributed learning framework in
which numerous wireless end-user devices can train a global model with the data remained …

Federated Learning (FL) is a platform for smart healthcare systems that use wearables and
other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer …

With the booming development of Internet-of-Things (IoT) and communication technologies
such as 5G, our future world is envisioned as an interconnected entity where billions of …

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine
Learning (ML), where the ML model is trained in a decentralized manner by the clients …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …

Over the past years, the emergence of intelligent networks empowered by machine learning
techniques has brought great facilitates to different aspects of human life. However, using …

With the vigorous development of deep learning, sharing trained deep neural network
(DNN) models has become a common trend in various fields. An urgent problem is to protect …

Explainable artificial intelligence (XAI) is gradually becoming a key component of many
artificial intelligence systems. However, such pursuit of transparency may bring potential …

Recently, membership inference attacks (MIAs) against machine learning models have been
proposed. Using MIAs, adversaries can inference whether a data record is in the training set …