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

Placeholder

Institution Authors

Research Projects

Authors

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

Journal Title

Journal ISSN

Volume Title

Type

Article

Access

info:eu-repo/semantics/restrictedAccess

Publication Status

Published

Journal Issue

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.

Date

2021-08

Publisher

IEEE

Description

Keywords

Citation


Page Views

0

File Download

0