PDM: Privacy-Aware Deployment of Machine-Learning Applications for Industrial Cyber–Physical Cloud Systems

Xiaolong Xu, Ruichao Mo, Xiaochun Yin, Mohanmmad R. Khosravi, Fahimeh Aghaei, Victor Chang, Guangshun Li

Research output: Contribution to journalArticlepeer-review

Abstract

The cyber–physical cloud systems (CPCSs) release powerful capability in provisioning the complicated industrial services. Due to the advances of machine learning (ML) in attack detection, a wide range of ML applications are involved in industrial CPCSs. However, how to ensure the implementation efficiency of these applications, and meanwhile avoid the privacy disclosure of the datasets due to data acquisition by different operators, remain challenging for the design of the CPCSs. To fill this gap, in this article a privacy-aware deployment method (PDM), named PDM, is devised for hosting the ML applications in the industrial CPCSs. In PDM, the ML applications are partitioned as multiple computing tasks with certain execution order, like workflows. Specifically, the deployment problem is formulated as a multiobjective problem for improving the implementation performance and resource utility. Then, the most balanced and optimal strategy is selected by leveraging an improved differential evolution technique. Finally, through comprehensive experiments and comparison analysis, PDM is fully evaluated.
Original languageEnglish
Pages (from-to)5819-5828
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number8
Early online date15 Oct 2020
DOIs
Publication statusPublished - 1 Aug 2021

Fingerprint

Dive into the research topics of 'PDM: Privacy-Aware Deployment of Machine-Learning Applications for Industrial Cyber–Physical Cloud Systems'. Together they form a unique fingerprint.

Cite this