Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications
The domain of healthcare data collaboration heralds an era of profound transformation, underscoring an exceptional potential to elevate the quality of patient care and expedite the advancement of medical research. The formidable challenge, however, lies in the safeguarding of sensitive information&a...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10201859/ |
| _version_ | 1797743467959418880 |
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| author | Mohammed Abaoud Muqrin A. Almuqrin Mohammad Faisal Khan |
| author_facet | Mohammed Abaoud Muqrin A. Almuqrin Mohammad Faisal Khan |
| author_sort | Mohammed Abaoud |
| collection | DOAJ |
| description | The domain of healthcare data collaboration heralds an era of profound transformation, underscoring an exceptional potential to elevate the quality of patient care and expedite the advancement of medical research. The formidable challenge, however, lies in the safeguarding of sensitive information’s privacy and security - a monumental task that creates significant obstacles. This paper presents an innovative approach designed to address these challenges through the implementation of privacy-preserving federated learning models, effectively pioneering a novel path in this intricate field of research. Our proposed solution enables healthcare institutions to collectively train machine learning models on decentralized data, concurrently preserving the confidentiality of individual patient data. During the model aggregation phase, the proposed mechanism enforces the protection of sensitive data by integrating cutting-edge privacy-preserving methodologies, including secure multi-party computation and differential privacy. To substantiate the efficacy of the proposed solution, we conduct an array of comprehensive simulations and evaluations with a concentrated focus on accuracy, computational efficiency, and privacy preservation. The results obtained corroborate that our methodology surpasses competing approaches in providing superior utility and ensuring robust privacy guarantees. The proposed approach encapsulates the feasibility of secure and privacy-preserving collaboration on healthcare data, serving as a compelling testament to its practicality and effectiveness. Through our work, we underscore the potential of harnessing collective intelligence in healthcare while maintaining paramount privacy protection, thereby affirming the promise of a new horizon in collaborative healthcare informatics. |
| first_indexed | 2024-03-12T14:55:50Z |
| format | Article |
| id | doaj.art-a7952df7d78f441ea7d99cbf55f18638 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:55:50Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a7952df7d78f441ea7d99cbf55f186382023-08-14T23:00:30ZengIEEEIEEE Access2169-35362023-01-0111835628357910.1109/ACCESS.2023.330116210201859Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare ApplicationsMohammed Abaoud0Muqrin A. Almuqrin1Mohammad Faisal Khan2https://orcid.org/0000-0001-5053-5028Department of Mathematics and Statistics, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaDepartment of Mathematics, College of Science in Zulfi, Majmaah University, Al-Majmaah, Saudi ArabiaDepartment of Basic Sciences, College of Science and Theoretical studies, Saudi Electronic University, Riyadh, Saudi ArabiaThe domain of healthcare data collaboration heralds an era of profound transformation, underscoring an exceptional potential to elevate the quality of patient care and expedite the advancement of medical research. The formidable challenge, however, lies in the safeguarding of sensitive information’s privacy and security - a monumental task that creates significant obstacles. This paper presents an innovative approach designed to address these challenges through the implementation of privacy-preserving federated learning models, effectively pioneering a novel path in this intricate field of research. Our proposed solution enables healthcare institutions to collectively train machine learning models on decentralized data, concurrently preserving the confidentiality of individual patient data. During the model aggregation phase, the proposed mechanism enforces the protection of sensitive data by integrating cutting-edge privacy-preserving methodologies, including secure multi-party computation and differential privacy. To substantiate the efficacy of the proposed solution, we conduct an array of comprehensive simulations and evaluations with a concentrated focus on accuracy, computational efficiency, and privacy preservation. The results obtained corroborate that our methodology surpasses competing approaches in providing superior utility and ensuring robust privacy guarantees. The proposed approach encapsulates the feasibility of secure and privacy-preserving collaboration on healthcare data, serving as a compelling testament to its practicality and effectiveness. Through our work, we underscore the potential of harnessing collective intelligence in healthcare while maintaining paramount privacy protection, thereby affirming the promise of a new horizon in collaborative healthcare informatics.https://ieeexplore.ieee.org/document/10201859/Privacy-preservingfederated learninghealthcare datadifferential privacysecure multi-party computationmachine learning |
| spellingShingle | Mohammed Abaoud Muqrin A. Almuqrin Mohammad Faisal Khan Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications IEEE Access Privacy-preserving federated learning healthcare data differential privacy secure multi-party computation machine learning |
| title | Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications |
| title_full | Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications |
| title_fullStr | Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications |
| title_full_unstemmed | Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications |
| title_short | Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications |
| title_sort | advancing federated learning through novel mechanism for privacy preservation in healthcare applications |
| topic | Privacy-preserving federated learning healthcare data differential privacy secure multi-party computation machine learning |
| url | https://ieeexplore.ieee.org/document/10201859/ |
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