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Certificateless data integrity auditing with sparse Merkle trees for the cloud-edge environment.

Ruizhong Du1,2, Ziyuan Wang3, Jiawei Shen4

  • 1School of Cyber Security and Computer, Hebei University, Baoding, 071000, China.

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Summary

This study introduces a certificateless auditing scheme for cloud-edge IoT, reducing computation and communication overhead. It enhances data integrity and security without certificate management, improving efficiency for smart cities.

Keywords:
Certificateless public key cryptographyData integrity auditingEdge computingLow complexityOnline/offline signature

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Area of Science:

  • Cybersecurity
  • Cloud Computing
  • Internet of Things (IoT)

Background:

  • Data integrity in cloud-edge IoT is crucial but hindered by dynamic data and resource limits.
  • Existing solutions often face challenges with scalability and efficiency in dynamic environments.

Purpose of the Study:

  • To propose a novel certificateless auditing scheme for cloud-edge environments.
  • To enhance data integrity while harmonizing cloud security with edge efficiency.

Main Methods:

  • Integration of online/offline cryptography and sparse Merkle trees.
  • Development of a certificateless auditing scheme with decentralized trust mechanisms.
  • Utilizing pre-download mechanisms for communication overhead reduction.

Main Results:

  • Significant reduction in user-side computation through offline/edge-side tag generation.
  • [Formula: see text] dynamic update complexity compared to traditional [Formula: see text] methods.
  • Achieved 75% communication overhead savings.
  • Demonstrated resilience against Key Generation Centre (KGC) collusion and tag forgery.
  • Faster audit times with sub-second latency for over 500k operations.

Conclusions:

  • The proposed scheme effectively addresses data integrity challenges in cloud-edge IoT ecosystems.
  • It offers improved scalability and real-time performance for applications like smart cities and Industry 4.0.
  • The framework supports future extensions in machine learning-optimized caching and blockchain trust models.