Publication:
PDM: Privacy-aware deployment of machine-learning applications for industrial cyber–physical cloud systems

dc.contributor.authorXu, X.
dc.contributor.authorMo, R.
dc.contributor.authorYin, X.
dc.contributor.authorKhosravi, M. R.
dc.contributor.authorHosseinabadi, Fahimeh Aghaei
dc.contributor.authorChang, V.
dc.contributor.authorLi, G.
dc.contributor.ozugradstudentHosseinabadi, Fahimeh Aghaei
dc.date.accessioned2023-05-03T06:48:06Z
dc.date.available2023-05-03T06:48:06Z
dc.date.issued2021-08
dc.description.abstractThe 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.en_US
dc.description.sponsorshipFinancial and Science Technology Plan Project of Xinjiang Production and Construction Corps ; National Natural Science Foundation of China (NSFC) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund
dc.identifier.doi10.1109/TII.2020.3031440en_US
dc.identifier.endpage5828en_US
dc.identifier.issn1551-3203en_US
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85105603236
dc.identifier.startpage5819en_US
dc.identifier.urihttp://hdl.handle.net/10679/8164
dc.identifier.urihttps://doi.org/10.1109/TII.2020.3031440
dc.identifier.volume17en_US
dc.identifier.wos000647406400068
dc.language.isoengen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Transactions on Industrial Informatics
dc.relation.publicationcategoryInternational Refereed Journal
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsCyber-physical cloud systems (CPCSs)en_US
dc.subject.keywordsMachine learning (ML)en_US
dc.subject.keywordsNondominated sorting differential evolution (NSDE)en_US
dc.subject.keywordsPrivacy-aware deploymenten_US
dc.titlePDM: Privacy-aware deployment of machine-learning applications for industrial cyber–physical cloud systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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