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

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.

Publication DOI: https://doi.org/10.1109/TII.2020.3031440
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Funding Information: Manuscript received June 29, 2020; revised August 16, 2020, September 13, 2020, September 29, 2020, and October 10, 2020; accepted October 12, 2020. Date of publication October 15, 2020; date of current version May 3, 2021. This work was supported in part
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding Information: This work was supported in part by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps under Grant 2020DB005 in part by the National Natural Science Foundation of China under Grant 61702277, and in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.
Uncontrolled Keywords: Cyber-physical cloud systems (CPCSs),machine learning (ML),nondominated sorting differential evolution (NSDE),privacy-aware deployment,Control and Systems Engineering,Information Systems,Computer Science Applications,Electrical and Electronic Engineering
Publication ISSN: 1551-3203
Last Modified: 15 Apr 2024 07:42
Date Deposited: 09 Jun 2022 10:50
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ieeexpl ... ocument/9226095 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-08-01
Published Online Date: 2020-10-15
Accepted Date: 2020-10-12
Authors: Xu, Xiaolong
Mo, Ruichao
Yin, Xiaochun
Khosravi, Mohanmmad R.
Aghaei, Fahimeh
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Li, Guangshun

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