The effectiveness of state-of-the-art deep learning (DL) models has empowered the
development of industrial Internet of things (IIoT). Recently, considering resource …

Nowadays, IoT networks and devices exist in our everyday life, capturing and carrying
unlimited data. However, increasing penetration of connected systems and devices implies …

Artificial Intelligence (AI) technology has been widely applied to Internet of Thing (IoT) and
one of key application is intelligence data collection from billions of IoT devices. However …

The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast
number of connected devices and the lack of security features. Attacks in edge IIoT can lead …

Billions of IoT devices are connected to networks all around us, enabling cyber-physical
systems. These devices can carry and generate user-sensitive data, examples of such …

Today, the architectures for efficient and secure network system designs, such as Internet of
Things (IoT) and big data analytics, are growing at a faster pace than ever before. Edge …

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT)
devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent …

The increase of the computational power in edge devices has enabled the penetration of
distributed machine learning technologies such as federated learning, which allows to build …

Edge computing combining with artificial intelligence (AI) has enabled the timely processing
and analysis of streaming data produced by IoT intelligent applications. However, it causes …

This paper proposes a new confidential deep learning (DL) inference system with ARM
TrustZone to provide confidentiality and integrity of DL models and data in an untrusted …