BSeIn: A blockchain-based secure mutual authentication with fine-grained access control system for industry 4.0

By Chao Lin, De-biao He, Xinyi Huang, Kim-Kwang Raymond Choo, Athanasios V. Vasilakos. Journal of Network and Computer Applications, 2018

365 citations recorded by the source corpus.

Abstract To be prepared for the ‘Industry 4.0’-era, we propose a hierarchical framework comprising four tangible layers, which is designed to vertically integrate inter-organizational value networks, engineering value chain, manufacturing factories, etc. The conceptual framework allows us to efficiently implement a flexible and reconfigurable smart factory. However, we need to consider security inherent in existing (stand-alone) devices and networks as well as those that may arise in such integrations. Especially the existing solutions are insufficient to address these fundamental security concerns. Thus, we present a blockchain-based system for secure mutual authentication, BSeIn, to enforce fine-grained access control polices. The proposed system (with integrated attribute signature, multi-receivers encryption and message authentication code) is designed to provide privacy and security guarantees such as anonymous authentication, auditability, and confidentiality. BSeIn also scales well due to the utilization of smart contract. We then evaluate the security and performance of BSeIn. For example, findings from the performance evaluation demonstrate that Initialization/Request Issuance/Chain Transaction/State Delivery/Permission Update phase only cost 12.123/4.810/6.978/0.013/2.559s, respectively.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f67a89eaa58a2b2b4552b22279305ebfb6e8a0fc

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Citations in the corpus

Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective (2020, neutral): “[67] presented a blockchain-based system for secure mutual authentication to enforce ﬁ ne-grained access control polices.”
Blockchain Technologies for the Internet of Things: Research Issues and Challenges (2018, methodology): “By utilizing statistical analysis method, Gu et al. [46] introduced a malware detection system based on the consortium Blockchain, named CB-MDEE, which is composed of detecting consortium chain by test members and public chain by users.”