Edge computing has emerged as a popular computing paradigm with the aim to minimize the delay between end-users and the cloud. A wide range of artificial intelligence applications benefit from the low latency offered by edge computing, e.g., driverless cars, smart homing, etc. However, edge computing suffers from capacity limitations and is challenged by computationally expensive artificial intelligent applications. To overcome these limitations, the edge-cloud computing paradigm offers a trade-off between artificial intelligence applications, requirements for computing resources and low latency. Unlike the centralized cloud, in the edge-cloud environment, there is a lack of security technologies and privacy protection mechanisms specifically designed for artificial intelligence applications facilitated by the edge-cloud computing paradigm. In the edge-cloud environment, artificial intelligence applications are subject to a variety of security threats, such as data privacy disclosure, adversarial attacks, confidential attacks, etc. This thematic series shares and discusses recent advances and future trends of security and privacy issues for artificial intelligence in edge-cloud computing.
Guest editors:
Lianyong Qi, Qufu Normal University, China
Jianxin Li, Beihang University, China
Nick Antonopoulos, Edinburgh Napier University, UK
Hao Wang, Norwegian University of Science and Technology, Norway