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<p>Rare genetic diseases affect 5–8% of the population and often lead to prolonged diagnostic delays due to their complex, multisystem manifestations. Leveraging Electronic Health Records (EHRs) can accelerate diagnosis, but challenges such as data complexity, limited interoperability, and privacy concerns hinder effective integration—particularly in IoT-based, resource-constrained environments. To address these challenges, we propose StegoSec-EHR, a novel blockchain-enabled IoT framework that secures EHRs and supports accurate genetic disease diagnosis. The framework combines distortion-minimizing steganography for embedding encrypted EHR data into medical images, lightweight signcryption for data confidentiality, and Hyperledger-based blockchain for tamper-proof decentralized access. For intelligent analytics, features are extracted using a U-Net model optimized with enhanced lizard optimization (ELO), and genetic diseases are classified via a dynamic coherent quantum neural network (DC-QNN). Evaluations on deidentified, structured records from 1.28 million patients across Singapore’s SingHealth cluster shows up to 98% improvement in security performance while maintaining high diagnostic accuracy. These results highlight the framework’s novelty and potential as a secure, scalable solution for modern healthcare.</p>

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StegoSec-EHR: A blockchain-enabled IoTFramework for secure HER sharing via steganography in genetic disease diagnosis

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Varun Malik,
  • Mudassir Khan,
  • Punam Bhoyar,
  • Kimmi Gupta,
  • Mohammed Ashfaq Hussain,
  • Kavita Arora,
  • Barga Mohammed Mujahid

摘要

Rare genetic diseases affect 5–8% of the population and often lead to prolonged diagnostic delays due to their complex, multisystem manifestations. Leveraging Electronic Health Records (EHRs) can accelerate diagnosis, but challenges such as data complexity, limited interoperability, and privacy concerns hinder effective integration—particularly in IoT-based, resource-constrained environments. To address these challenges, we propose StegoSec-EHR, a novel blockchain-enabled IoT framework that secures EHRs and supports accurate genetic disease diagnosis. The framework combines distortion-minimizing steganography for embedding encrypted EHR data into medical images, lightweight signcryption for data confidentiality, and Hyperledger-based blockchain for tamper-proof decentralized access. For intelligent analytics, features are extracted using a U-Net model optimized with enhanced lizard optimization (ELO), and genetic diseases are classified via a dynamic coherent quantum neural network (DC-QNN). Evaluations on deidentified, structured records from 1.28 million patients across Singapore’s SingHealth cluster shows up to 98% improvement in security performance while maintaining high diagnostic accuracy. These results highlight the framework’s novelty and potential as a secure, scalable solution for modern healthcare.