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Secured Secret Sharing of QR Codes Based on Nonnegative Matrix Factorization and Regularized Super Resolution

Ramesh Velumani1, Hariharasitaraman Sudalaimuthu2, Gaurav Choudhary3

  • 1Institute of Electrical and Electronics Engineers (IEEE), Aruppukottai 626101, India.

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Summary

This study introduces a new method for securely sharing Quick Response (QR) codes using Nonnegative Matrix Factorization (NMF) and Super Resolution Convolutional Neural Networks (SRCNN). The approach enhances QR code security against tampering and duplication for data transfer.

Keywords:
Nonnegative Matrix Factorizationbasis matrixcoefficient matrixconvolutional neural networkquick response codesecret sharingstructural regularizationsuper resolution

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Area of Science:

  • Computer Science
  • Information Security
  • Data Science

Background:

  • Quick Response (QR) codes are widely used for information exchange and in healthcare IoT applications.
  • The open-source nature of QR code generators and scanners makes them vulnerable to duplication and tampering.
  • Existing security schemes for QR codes have limitations.

Purpose of the Study:

  • To present a novel (n,n) secret-sharing scheme for secure QR code transfer.
  • To enhance the security and integrity of QR code data.
  • To offer an alternative to polynomial and visual cryptography-based schemes.

Main Methods:

  • A novel (n,n) secret-sharing scheme based on Nonnegative Matrix Factorization (NMF).
  • QR code shares are reconstructed using a regularized Super Resolution Convolutional Neural Network (SRCNN).
  • Exploitation of NMF for part-based data representation and SRCNN for structural element capture.

Main Results:

  • The proposed method provides a potential solution for secured exchange of QR codes with varying error correction levels.
  • Experimental results demonstrate significant computational and combinatorial hurdles for adversaries attempting to compromise the system.
  • Security analysis shows an adversary needs 2^58 additional share combinations and 3 × 2^88 additional computations compared to a representative approach.

Conclusions:

  • The proposed NMF-based secret-sharing scheme with SRCNN reconstruction offers enhanced security for QR code data transfer.
  • The method effectively addresses vulnerabilities associated with QR code duplication and tampering.
  • This approach represents a significant advancement in securing information exchange via QR codes in various applications, including IoT.