Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation
<p dir="ltr">Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medica...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2023
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513531021361152 |
|---|---|
| author | Sherif Abdelfattah (17714523) |
| author2 | Mohamed Baza (17714526) Mohamed Mahmoud (4544233) Mostafa M. Fouda (14768509) Khalid Abualsaud (16888701) Elias Yaacoub (14150586) Maazen Alsabaan (17714529) Mohsen Guizani (12580291) |
| author2_role | author author author author author author author |
| author_facet | Sherif Abdelfattah (17714523) Mohamed Baza (17714526) Mohamed Mahmoud (4544233) Mostafa M. Fouda (14768509) Khalid Abualsaud (16888701) Elias Yaacoub (14150586) Maazen Alsabaan (17714529) Mohsen Guizani (12580291) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sherif Abdelfattah (17714523) Mohamed Baza (17714526) Mohamed Mahmoud (4544233) Mostafa M. Fouda (14768509) Khalid Abualsaud (16888701) Elias Yaacoub (14150586) Maazen Alsabaan (17714529) Mohsen Guizani (12580291) |
| dc.date.none.fl_str_mv | 2023-11-08T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s23229033 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Lightweight_Multi-Class_Support_Vector_Machine-Based_Medical_Diagnosis_System_with_Privacy_Preservation/24921531 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning privacy preservation cloud security medical diagnosis support vector machine (SVM) multiclassification |
| dc.title.none.fl_str_mv | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients’ health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23229033" target="_blank">https://dx.doi.org/10.3390/s23229033</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_04ba2c05febcaddaca65ec7124adc082 |
| identifier_str_mv | 10.3390/s23229033 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24921531 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy PreservationSherif Abdelfattah (17714523)Mohamed Baza (17714526)Mohamed Mahmoud (4544233)Mostafa M. Fouda (14768509)Khalid Abualsaud (16888701)Elias Yaacoub (14150586)Maazen Alsabaan (17714529)Mohsen Guizani (12580291)Health sciencesHealth services and systemsInformation and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningprivacy preservationcloud securitymedical diagnosissupport vector machine (SVM)multiclassification<p dir="ltr">Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients’ health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23229033" target="_blank">https://dx.doi.org/10.3390/s23229033</a></p>2023-11-08T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s23229033https://figshare.com/articles/journal_contribution/Lightweight_Multi-Class_Support_Vector_Machine-Based_Medical_Diagnosis_System_with_Privacy_Preservation/24921531CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249215312023-11-08T03:00:00Z |
| spellingShingle | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation Sherif Abdelfattah (17714523) Health sciences Health services and systems Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning privacy preservation cloud security medical diagnosis support vector machine (SVM) multiclassification |
| status_str | publishedVersion |
| title | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| title_full | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| title_fullStr | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| title_full_unstemmed | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| title_short | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| title_sort | Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation |
| topic | Health sciences Health services and systems Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning privacy preservation cloud security medical diagnosis support vector machine (SVM) multiclassification |