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IoT 24(1):

Research Article

Leveraging AI and Blockchain for Privacy Preservation and Security in Fog Computing

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  • @ARTICLE{10.4108/eetiot.5555,
        author={S B Goyal and Anand Singh Rajawat and Manoj Kumar and Prerna Agarwal},
        title={Leveraging AI and Blockchain for Privacy Preservation and Security in Fog Computing},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2024},
        month={3},
        keywords={Artificial Intelligence, Fog computing, Privacy Preservation Model, Cloud Computing},
        doi={10.4108/eetiot.5555}
    }
    
  • S B Goyal
    Anand Singh Rajawat
    Manoj Kumar
    Prerna Agarwal
    Year: 2024
    Leveraging AI and Blockchain for Privacy Preservation and Security in Fog Computing
    IOT
    EAI
    DOI: 10.4108/eetiot.5555
S B Goyal1,*, Anand Singh Rajawat2, Manoj Kumar3, Prerna Agarwal4
  • 1: City University
  • 2: Sandip University
  • 3: University of Wollongong in Dubai
  • 4: Bennett University
*Contact email: drsbgoyal@gmail.com

Abstract

INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI). OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based. METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it.  RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced. CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.

Keywords
Artificial Intelligence, Fog computing, Privacy Preservation Model, Cloud Computing
Received
2023-12-22
Accepted
2024-03-19
Published
2024-03-26
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.5555

Copyright © 2024 S. B. Goyal et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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