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Security and privacy issues for artificial intelligence in edge-cloud computing

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

  1. Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the ...

    Authors: Xiaoping Zhao, Kaiyang Lv, Zhongyang Zhang, Yonghong Zhang and Yifei Wang

    Citation: Journal of Cloud Computing 2020 9:58

    Content type: Research

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  2. The video request service of users in 5G network will explode, and adaptive bit rate technology can provide users with reliable video response. Placing video resources on edge servers close to users can overco...

    Authors: Zhi Liu, Bo Qiao and Kui Fang

    Citation: Journal of Cloud Computing 2020 9:56

    Content type: Research

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  3. This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi ar...

    Authors: Meiyu Pang, Li Wang and Ningsheng Fang

    Citation: Journal of Cloud Computing 2020 9:52

    Content type: Research

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  4. Blockchain-based cloud application (BCP) is an emerging cloud application architecture. By moving trust-critical functions onto blockchain, BCP offers unprecedented function transparency and data integrity. Et...

    Authors: Chao Liu, Jianbo Gao, Yue Li, Huihui Wang and Zhong Chen

    Citation: Journal of Cloud Computing 2020 9:35

    Content type: Research

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  5. Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an ...

    Authors: Wei Tian, Lei Yi, Wei Liu, Wei Huang, Guangyi Ma and Yonghong Zhang

    Citation: Journal of Cloud Computing 2020 9:22

    Content type: Research

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  6. Power grid dispatching is among the forefront issues in the power industry for it can highly influence the efficiency of electricity-related industries. At present, power grid dispatching is usually managed ma...

    Authors: Xianrui Yang, Yuming Liu, Jiehong Wang, Zhao Yao, Yanping Zhou and Shucun Fu

    Citation: Journal of Cloud Computing 2020 9:20

    Content type: Research

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