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SVIoT: A Secure Visual-IoT Framework for Smart Healthcare.

Javaid A Kaw1, Solihah Gull1, Shabir A Parah1

  • 1Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar 190006, India.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary

This study introduces SVIoT, a Secure Visual-IoT framework for healthcare, using reversible data hiding to secure patient data and reduce bandwidth needs. The novel approach enhances security and efficiency in visual Internet of Things applications.

Keywords:
bandwidthcomputational complexityinformation hidingreversibilitysecurityvisual-IoT

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Healthcare Technology

Background:

  • The Internet of Things (IoT) integrates digital information into the physical world, with visual data being crucial in sectors like healthcare.
  • Visual-IoT systems face challenges in data security, large bandwidth requirements, and the communication-computation gap, especially for sensitive patient data transmission.
  • Existing solutions often struggle to balance security, efficiency, and data integrity in visual IoT healthcare applications.

Purpose of the Study:

  • To present SVIoT, a Secure Visual-IoT framework designed to address data security and resource constraints in IoT-based healthcare.
  • To propose a novel reversible data hiding (RDH) scheme to enhance the security and efficiency of visual data transmission.
  • To improve the perceptual quality and authentication of embedded data in visual IoT systems.

Main Methods:

  • Developed a novel reversible data hiding (RDH) scheme based on One Dimensional Neighborhood Mean Interpolation (ODNMI) to reduce computational complexity and bandwidth requirements.
  • Implemented a Left Data Shifting (LDS) mechanism before data embedding to improve perceptual quality at high payloads.
  • Utilized AES-128 encryption for enhanced data security and embedded a fragile watermark for content authentication.

Main Results:

  • The ODNMI-based RDH scheme reduced computational complexity and storage/bandwidth requirements by 50 percent.
  • Achieved an average Peak Signal-to-Noise Ratio (PSNR) of 43 dB for a payload of 1.5 bits per pixel (bpp), indicating high visual quality.
  • The SVIoT framework successfully ensured data security, authentication, and efficient transmission of visual data in a healthcare context.

Conclusions:

  • The proposed SVIoT framework effectively addresses critical security and resource challenges in visual IoT for healthcare.
  • The novel RDH scheme with ODNMI and LDS offers a significant improvement in efficiency and perceptual quality for secure data embedding.
  • This research contributes a robust solution for secure and efficient visual data management in the burgeoning field of IoT-enabled healthcare.