Privacy-Preserving Microservices in Industrial Internet-of-Things-Driven Smart Applications

被引:21
作者
Bugshan, Neda [1 ]
Khalil, Ibrahim [1 ]
Moustafa, Nour [2 ]
Rahman, Mohammad Saidur [1 ]
机构
[1] RMIT Univ, Sch Comp Technol, Melbourne, Vic 3000, Australia
[2] Univ New South Wales Canberra, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia
基金
澳大利亚研究理事会;
关键词
Industrial Internet of Things; Medical services; Cloud computing; Data models; Computer architecture; Task analysis; Servers; Differential privacy (DP); edge computing; Industrial Internet of Things (IIoT); microservices; privacy-preserving machine learning (ML); smart healthcare; ARCHITECTURE; SELECTION; SCHEME;
D O I
10.1109/JIOT.2021.3098980
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Machine learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service quality, along with fast implementation and integration with the edge and cloud services. However, the execution of ML in an edge-cloud environment introduces privacy risks to data owners (e.g., patients). In this article, we present a privacy-preserving ML framework by leveraging microservice technology for safeguarding healthcare IIoT systems. More specifically, we develop a microservice-based distributed privacy-preserving technique using differential privacy (DP) and a radial basis function network (RBFN) to balance between privacy protection and model performance in edge networks. We conduct extensive experiments to evaluate the performance of the proposed technique. The results revealed that DP has a significant influence on the model's performance and achieves more than 90% accuracy with an epsilon value over 0.4, enhancing data protection and analytics through the implementation of microservices.
引用
收藏
页码:2821 / 2831
页数:11
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