Open Access

ARTICLE

Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

Xiangmin Guo^1,2, Guangjun Liang^1,2,*, Jiayin Liu^1,2, Xianyi Chen^3,*

1 Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing, 210031, China
2 Jiangsu Electronic Data Forensics and Analysis Engineering Research Center, Jiangsu Police Institute, Nanjing, 210031, China
3 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Authors: Guangjun Liang. Email: ,; Xianyi Chen. Email:

(This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)

Computers, Materials & Continua 2023, 77(3), 3305-3323. https://doi.org/10.32604/cmc.2023.044529

Received 01 August 2023; Accepted 23 October 2023; Issue published 26 December 2023

Abstract

Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things (IoT) in the cloud. HD2C is developed by combining Federated Learning (FL) with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority. The “Data Island” problem can be solved by FL’s emphasis on privacy and lightning-fast processing, while Blockchain provides a decentralized incentive structure that is impervious to poisoning. FL with Blockchain allows quick consensus through smart member selection and verification. The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing. Extensive analysis results derived from IIoT datasets confirm HD2C superiority. When compared to other consensus algorithms, the Blockchain PoA’s foundational cost is significant. The accuracy and memory utilization evaluation results predict the total benefits of the system. In comparison to the ɳ values 0.004 and 0.04, the ɳ value of 0.4 achieves good accuracy. According to the experiment results, the number of transactions per second has minimal impact on memory requirements. The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

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