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Xi Zheng
Researcher at Macquarie University
Publications - 147
Citations - 2714
Xi Zheng is an academic researcher from Macquarie University. The author has contributed to research in topics: Computer science & Edge computing. The author has an hindex of 21, co-authored 123 publications receiving 1376 citations. Previous affiliations of Xi Zheng include South University of Science and Technology of China & Alibaba Group.
Papers
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Journal ArticleDOI
Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud
TL;DR: Experimental results show that multidimensional data cleaning based on mobile edge nodes improves the efficiency of data cleaning while maintaining data reliability and integrity, and greatly reduces the bandwidth and energy consumption of the industrial SCS.
Journal ArticleDOI
Crowdsourcing Mechanism for Trust Evaluation in CPCS Based on Intelligent Mobile Edge Computing
TL;DR: Results corroborate that the proposed mechanisms can efficiently stimulate mobile edge users to perform evaluation task and improve the accuracy of trust evaluation, and validate the validity of Quality-Aware Trustworthy Incentive Mechanism.
Journal ArticleDOI
A survey on security issues in services communication of Microservices-enabled fog applications
TL;DR: A survey of different security risks that pose a threat to the Microservices‐based fog applications is presented and an ideal solution for security issues in services communication of Micro services‐based Fog Services architecture is proposed.
Proceedings ArticleDOI
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
TL;DR: This paper presents an in-depth analysis of five adversarial attacks and four defense methods on three driving models and derives several implications for system and middleware builders.
Journal ArticleDOI
Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing
TL;DR: The experimental results demonstrate that the chain-PPFL scheme can achieve practical privacy preservation (equivalent to differential privacy with $\epsilon $ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.