4.8 Article

Lightweight Mutual Authentication and Privacy-Preservation Scheme for Intelligent Wearable Devices in Industrial-CPS

期刊

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 8, 页码 5829-5839

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3043802

关键词

Authentication; Servers; Security; Wearable computers; Hidden Markov models; Hospitals; Encryption; Artificial intelligence (AI); authentication; client-server model; industrial cyber-physical systems (I-CPS); Industrial Internet of Things (IIoT); privacy; security

资金

  1. NIH [P20GM109090]
  2. Collaboration Initiative of the University of Nebraska system
  3. pilot award from the Center for Research in Human Movement Variability

向作者/读者索取更多资源

Industry 5.0 encompasses the digitalization, automation, and data exchange in industrial processes involving AI, IIoT, and I-CPS. In healthcare, I-CPS enables intelligent wearable devices to gather and transmit data supporting decision-making with innovative applications. However, challenges such as privacy risks for wearable devices in I-CPS highlight the need for lightweight mutual authentication schemes to address security concerns.
Industry 5.0 is the digitalization, automation, and data exchange of industrial processes that involve artificial intelligence, industrial Internet of Things (IIoT), and industrial cyber-physical systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, in this article we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled hidden Markov model. We analyzed the robustness and security of our scheme using Burrows-Abadi-Needham logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.

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