Disease Prediction in Edge Computing: A Privacy-Preserving Technique for PHI Collection and Analysis

Authors

Liehuang Zhu, School of Cyberspace Science and Technology at Beijing Institute of Technology
Chuan Zhang, School of Cyberspace Science and Technology at Beijing Institute of Technology
Chang Xu, School of Cyberspace Science and Technology at Beijing Institute of Technology
Wei Wang, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation at Beijing Jiaotong University
Xiaojiang Du, Department of Electrical and Computer Engineering at Stevens Institute of Technology
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow
Kashif Sharif, School of Computer Science and Technology at Beijing Institute of Technology

Document Type

Article

Publication Title

IEEE Network

Abstract

Edge computing has garnered significant attention in recent years, as it enables the extension of cloud resources to the network edge. This enables the user to utilize virtually enhanced resources in terms of storage and computation at a lower cost. The edge computing-assisted wireless wearable communication (EWWC) technology is a prime candidate for e-Health edge applications to collect personal health information (PHI), which leads to disease learning and prediction. Ensuring privacy and efficiency of such system in EWWC is extremely important. In this article, we introduce an efficient and privacypreserving disease prediction scheme. We use the randomizable signature and matrices encryption technique to achieve identity protection and data privacy. The experimental analysis shows that our solution outperforms the existing solution in terms of computational costs and communication overhead. At the same time is able to provide data privacy, prediction model security, user identity protection, mendacious data traceability, and model verifiability. We also analyze potential future research directions related to this emerging area. IEEE

First Page

1

Last Page

7

DOI

10.1109/MNET.001.1800162

Publication Date

8-8-2022

Keywords

Biomedical monitoring, Communication system security, Diseases, Security, Wearable computers, Wireless communication, Wireless sensor networks, Cost benefit analysis, Cryptography, Data privacy, Digital storage, Edge computing, Forecasting, Network security

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Recommended Citation

L. Zhu et al., "Disease Prediction in Edge Computing: A Privacy-Preserving Technique for PHI Collection and Analysis," in IEEE Network, doi: 10.1109/MNET.001.1800162.