A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System
Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10097733/ |
| _version_ | 1797655669188329472 |
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| author | Reyazur Rashid Irshad Shahab Saquib Sohail Shahid Hussain Dag Oivind Madsen Mohammed Altaf Ahmed Ahmed Abdu Alattab Omar Ali Saleh Alsaiari Khalid Ahmed Abdallah Norain Abdallah Ahmed Alzupair Ahmed |
| author_facet | Reyazur Rashid Irshad Shahab Saquib Sohail Shahid Hussain Dag Oivind Madsen Mohammed Altaf Ahmed Ahmed Abdu Alattab Omar Ali Saleh Alsaiari Khalid Ahmed Abdallah Norain Abdallah Ahmed Alzupair Ahmed |
| author_sort | Reyazur Rashid Irshad |
| collection | DOAJ |
| description | Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms. |
| first_indexed | 2024-03-11T17:17:52Z |
| format | Article |
| id | doaj.art-c60cd9bc08a641ecb91fb5382e5668f5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c60cd9bc08a641ecb91fb5382e5668f52023-10-19T23:01:07ZengIEEEIEEE Access2169-35362023-01-011111341011342110.1109/ACCESS.2023.326595410097733A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud SystemReyazur Rashid Irshad0Shahab Saquib Sohail1https://orcid.org/0000-0002-5944-7371Shahid Hussain2Dag Oivind Madsen3https://orcid.org/0000-0001-8735-3332Mohammed Altaf Ahmed4https://orcid.org/0000-0003-0355-7835Ahmed Abdu Alattab5https://orcid.org/0000-0001-8463-7428Omar Ali Saleh Alsaiari6Khalid Ahmed Abdallah Norain7Abdallah Ahmed Alzupair Ahmed8Department of Computer Science, College of Science and Arts Sharoura, Najran University, Najran, Saudi ArabiaDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, IndiaInnovative Value Institute, School of Business, Maynooth, W23, IrelandUSN School of Business, University of South-Eastern Norway, Hønefoss, NorwayDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Science, College of Science and Arts Sharoura, Najran University, Najran, Saudi ArabiaDepartment of Computer Science, College of Science and Arts Sharoura, Najran University, Najran, Saudi ArabiaDepartment of Computer Science, College of Science and Arts Sharoura, Najran University, Najran, Saudi ArabiaDepartment of Computer Science, College of Science and Arts Sharoura, Najran University, Najran, Saudi ArabiaCloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms.https://ieeexplore.ieee.org/document/10097733/HealthcareBFL-PSOsanitizationrestorationcloud storagedegree of modification |
| spellingShingle | Reyazur Rashid Irshad Shahab Saquib Sohail Shahid Hussain Dag Oivind Madsen Mohammed Altaf Ahmed Ahmed Abdu Alattab Omar Ali Saleh Alsaiari Khalid Ahmed Abdallah Norain Abdallah Ahmed Alzupair Ahmed A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System IEEE Access Healthcare BFL-PSO sanitization restoration cloud storage degree of modification |
| title | A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System |
| title_full | A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System |
| title_fullStr | A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System |
| title_full_unstemmed | A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System |
| title_short | A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System |
| title_sort | multi objective bee foraging learning based particle swarm optimization algorithm for enhancing the security of healthcare data in cloud system |
| topic | Healthcare BFL-PSO sanitization restoration cloud storage degree of modification |
| url | https://ieeexplore.ieee.org/document/10097733/ |
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