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<p>This paper presents a hybrid security model that combines the honeybee optimization algorithm with quantum cryptography. Quantum cryptography ensures the secure generation and distribution of encryption keys within healthcare systems, mitigating vulnerabilities. The integration of Honeybee optimization enhances system performance by optimizing memory usage, encryption, decryption, and processing times in the cloud system. The honeybee fitness function accurately estimates the integrity of encrypted data by comparing it with the original hash value. Additionally, a user verification module is developed to authenticate authorized users, prevent unauthorized data access, and preserve the system’s computational and storage efficiencies. The proposed framework is applied to an open-access smart healthcare dataset, achieving a confidentiality rate of 98.00%, a data integrity rate of 99.00%, an error rate of 0.12%, memory usage of 7.93&#xa0;KB, and encryption, decryption, and processing times of 4.65&#xa0;ms, 4.48&#xa0;ms, and 12.86&#xa0;ms, respectively. These results are further validated through statistical and computational complexity analysis.</p>

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Securing IoMT-based healthcare systems with meta-heuristic quantum cryptography and honeybee optimization

  • 期刊论文

  • 发表日期：

    2025年11月20日

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  • Mohamed A. G. Hazber,
  • Abdulrahman Albarrak,
  • Mohammed Altamimi,
  • Anandhavalli Muniasamy,
  • Asharul Islam,
  • Mohammed Altaf Ahmed,
  • Khaled M. Alalayah,
  • Shahid Hussain,
  • Reyazur Rashid Irshad

摘要

This paper presents a hybrid security model that combines the honeybee optimization algorithm with quantum cryptography. Quantum cryptography ensures the secure generation and distribution of encryption keys within healthcare systems, mitigating vulnerabilities. The integration of Honeybee optimization enhances system performance by optimizing memory usage, encryption, decryption, and processing times in the cloud system. The honeybee fitness function accurately estimates the integrity of encrypted data by comparing it with the original hash value. Additionally, a user verification module is developed to authenticate authorized users, prevent unauthorized data access, and preserve the system’s computational and storage efficiencies. The proposed framework is applied to an open-access smart healthcare dataset, achieving a confidentiality rate of 98.00%, a data integrity rate of 99.00%, an error rate of 0.12%, memory usage of 7.93 KB, and encryption, decryption, and processing times of 4.65 ms, 4.48 ms, and 12.86 ms, respectively. These results are further validated through statistical and computational complexity analysis.