Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

The development of Internet of Things (IoT) brings new changes to various fields.
Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more …

There are three key roles in Intelligent Transportation Systems (ITS): driver, vehicle and
road. However, existing static interactions among Driver-Vehicle-Road (DVR) are too …

Owning to the massive growth in internet connectivity, smartphone technology, and digital
tools, the use of various online social networks (OSNs) has significantly increased. On the …

With the development of social networks, more and more data about users are released on
social platforms such as Facebook, Enron, WeChat, in terms of social graphs. Without the …

The maturity in information technology and new energy technologies enables participants to
generate, buy, and sell energy in energy trading systems. Although applying blockchain …

Collaborative inference has recently emerged as an attractive framework for applying deep
learning to Internet of Things (IoT) applications by splitting a DNN model into several subpart …

Abstract The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by
enabling highly customised services and applications, which create huge volume and …

It is enormously challenging to achieve a satisfactory balance between quality of service
(QoS) and users' privacy protection along with measuring privacy disclosure in social …