EBIAS: ECC-enabled blockchain-based identity authentication scheme for IoT device

Wenyue Wang , Biwei Yan , Baobao Chai , Ruiyao Shen , Anming Dong , Jiguo Yu

High-Confidence Computing ›› 2025, Vol. 5 ›› Issue (1) : 100240

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High-Confidence Computing ›› 2025, Vol. 5 ›› Issue (1) : 100240 DOI: 10.1016/j.hcc.2024.100240
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EBIAS: ECC-enabled blockchain-based identity authentication scheme for IoT device

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Abstract

In the Internet of Things (IoT), a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network. However, devices face the challenge of a single point of failure, a malicious user may forge device identity to gain access and jeopardize system security. In addition, devices collect and transmit sensitive data, and the data can be accessed or stolen by unauthorized user, leading to privacy breaches, which posed a significant risk to both the confidentiality of user information and the protection of device integrity. Therefore, in order to solve the above problems and realize the secure transmission of data, this paper proposed EBIAS, a secure and efficient blockchain-based identity authentication scheme designed for IoT devices. First, EBIAS combined the Elliptic Curve Cryptography (ECC) algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data. Second, EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data. Finally, we performed security analysis and conducted sufficient experiment. The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes, which further proves the feasibility and effectiveness of EBIAS.

Keywords

IoT / Blockchain / ECC / Identity authentication / Security

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Wenyue Wang, Biwei Yan, Baobao Chai, Ruiyao Shen, Anming Dong, Jiguo Yu. EBIAS: ECC-enabled blockchain-based identity authentication scheme for IoT device. High-Confidence Computing, 2025, 5(1): 100240 DOI:10.1016/j.hcc.2024.100240

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CRediT authorship contribution statement

Wenyue Wang: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing-original draft. Biwei Yan: Methodology, Writing-review & editing, Supervision. Baobao Chai: Conceptualization, Formal analysis, Investigation, Supervision. Ruiyao Shen: Methodology, Formal analysis, Visualization. Anming Dong: Writing-review & editing, Supervision. Jiguo Yu: Writing-review & editing, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Science Foundation of China (62272256, 62202250), the Major Program of Shandong Provincial Natural Science Foundation for the Fundamental Research (ZR2022ZD03), the National Science Foundation of Shan-dong Province (ZR2021QF079), the Talent Cultivation Promotion Program of Computer Science and Technology in Qilu University of Technology (Shandong Academy of Sciences) (2023PY059), the Pilot Project for Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) (2022XD001), and the Colleges and Universities 20 Terms Foundation of Jinan City (202228093).

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