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DOI: 10.14569/IJACSA.2022.0131228
PDF

Fast Comprehensive Secret Sharing using Naive Image Compression

Author 1: Heri Prasetyo
Author 2: Kukuh Caezaocta Prayuda

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: This paper presents a simple method for performing (k,n)-Secret Sharing (SS) with fast computation. It aims to reduce the computational time of the former scheme in the shadow generation process. The former scheme performs SS with the polynomial function computation by involving the color palette. The color palette transforms noisy-like shadow image into more meaningfull appearance. However, this scheme requires a high computational burden on this transformation process. The proposed method exploits naive image compression to decrease the required bit for representing a secret and cover image. It effectively avoids the color palette usage previously used by the former scheme. The proposed method produces a set of shadow images with a cover image-like appearance. In addition, the secret and cover image can be reconstructed by gathering at least k shadow images. As documented in the Experimental Results section, the proposed method yields a promising result in the (k,n)-SS with reduced computational time compared to that of the former scheme.

Keywords: Comprehensive; image compression; naïve; polynomial; secret sharing

Heri Prasetyo and Kukuh Caezaocta Prayuda, “Fast Comprehensive Secret Sharing using Naive Image Compression” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131228

@article{Prasetyo2022,
title = {Fast Comprehensive Secret Sharing using Naive Image Compression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131228},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131228},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Heri Prasetyo and Kukuh Caezaocta Prayuda}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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