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

Name: PDM: Privacy-aware deployment of machine-learning applications for industrial cyber–physical cloud systems
Author: Xu, X., Mo, R., Yin, X., Khosravi, M. R., Hosseinabadi, Fahimeh Aghaei, Chang, V., Li, G.

İsim	PDM: Privacy-aware deployment of machine-learning applications for industrial cyber–physical cloud systems
Yazar	Xu, X., Mo, R., Yin, X., Khosravi, M. R., Hosseinabadi, Fahimeh Aghaei, Chang, V., Li, G.
Basım Tarihi:	2021-08
Basım Yeri	- IEEE
Konu	Cyber-physical cloud systems (CPCSs), Machine learning (ML), Nondominated sorting differential evolution (NSDE), Privacy-aware deployment
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1551-3203
Kayıt Numarası	15b7ccba-9b9c-48f8-b431-0802b4002500
Tarih	2021-08
Notlar	Financial 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
Örnek Metin	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.
DOI	10.1109/TII.2020.3031440
Cilt	17

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

Yazar Xu, X., Mo, R., Yin, X., Khosravi, M. R., Hosseinabadi, Fahimeh Aghaei, Chang, V., Li, G.

Basım Tarihi 2021-08

Basım Yeri - IEEE

Konu Cyber-physical cloud systems (CPCSs), Machine learning (ML), Nondominated sorting differential evolution (NSDE), Privacy-aware deployment

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1551-3203

Kayıt Numarası 15b7ccba-9b9c-48f8-b431-0802b4002500

Tarih 2021-08

Notlar Financial 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

Örnek Metin 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.

DOI 10.1109/TII.2020.3031440

Cilt 17