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This study introduces a novel secret image sharing scheme using a maze matrix for secure data distribution in Internet of Things (IoT) applications. It features advanced cheat detection and efficient shadow image generation, enhancing data privacy.

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Area of Science:

  • Computer Science
  • Information Security
  • Cryptography

Background:

  • Secret image sharing is vital for data protection in Internet of Things (IoT) applications.
  • Current methods face challenges in efficient and secure distribution of shared secrets.
  • Protecting private data requires robust techniques for managing distributed information.

Purpose of the Study:

  • To propose a novel secret image sharing scheme utilizing a unique maze matrix.
  • To develop an effective two-layered cheat detection mechanism for enhanced security.
  • To improve the time efficiency of secret image sharing processes.

Main Methods:

  • A novel maze matrix guides the hiding of secret data into cover images to produce shadow images.
  • A two-layered cheat detection mechanism is implemented, enabling individual shadow tampering identification.
  • Lagrange polynomials are derived for efficient computation of shadow image pixel values.

Main Results:

  • The proposed scheme successfully generates pairs of image shadows for secret sharing.
  • The cheat detection mechanism effectively identifies tampered shadows, even without other shadow information.
  • The use of Lagrange polynomials significantly improves the time efficiency of the sharing process.

Conclusions:

  • The developed secret image sharing scheme is effective and efficient.
  • The novel maze matrix and associated cheat detection enhance data security in distributed systems.
  • The scheme offers a promising solution for secure data handling in IoT environments.