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Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel

Kamta Nath Mishra1, Vandana Bhattacharjee1, Shashwat Saket1

  • 1Department of Computer Science & Engineering, Birla Institute of Technology, Ranchi, India.

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Securing data in edge computing environments is crucial. This study combines Blockchain, machine learning, and federated learning to enhance data security and client privacy in distributed systems.

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Blockchain technologyEdge computingFederated learning systemsMachine learning techniquesVoting classifier

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Distributed Systems

Background:

  • Managing large datasets in single-server environments poses security challenges.
  • Vulnerable data in dynamic, distributed settings like Mobile Edge computing requires robust security measures.
  • Edge computing offers a decentralized platform for data processing and model training.

Purpose of the Study:

  • To propose and evaluate a novel model for enhancing data security in edge computing environments.
  • To mitigate risks associated with vulnerable data and data breaches in distributed systems.
  • To ensure client privacy while enabling collaborative data training.

Main Methods:

  • Integration of Blockchain technology for secure consensus and data integrity.
  • Application of machine learning classifiers and optimization techniques for data security.
  • Implementation of federated learning for decentralized model training on client data.
  • Utilization of two-factor authentication for data security surveillance.

Main Results:

  • The proposed model effectively enhances data security in an edge computing setting.
  • Client privacy was successfully maintained through the use of Blockchain servers.
  • Federated learning enabled secure training of segregated client data batches.
  • Demonstrated the feasibility of a Blockchain-based training model in edge environments.

Conclusions:

  • The combination of Edge computing, Blockchain, and machine learning significantly improves data security and mitigates breach risks.
  • Federated learning provides a viable approach for training shared data while preserving client privacy.
  • The developed model offers a secure and efficient solution for data management in smart edge devices.