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المؤلفAbdelfattah, Sherif
المؤلفBaza, Mohamed
المؤلفMahmoud, Mohamed
المؤلفFouda, Mostafa M
المؤلفAbualsaud, Khalid
المؤلفYaacoub, Elias
المؤلفAlsabaan, Maazen
المؤلفGuizani, Mohsen
تاريخ الإتاحة2024-03-26T11:56:47Z
تاريخ النشر2023
اسم المنشورSensors
المصدرScopus
الرقم المعياري الدولي للكتاب14248220
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/s23229033
معرّف المصادر الموحدhttp://hdl.handle.net/10576/53519
الملخصMachine 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.
راعي المشروعThis publication was made possible by Project number (RSPD2023R636), King Saud University, Saudi Arabia, and NPRP grant # 13-0205-200270 from the Qatar National Research Fund (QNRF), Qatar. The Open Access funding was provided by the Qatar National Library.
اللغةen
الناشرMultidisciplinary Digital Publishing Institute (MDPI)
الموضوعcloud security
medical diagnosis
multiclassification
privacy preservation
support vector machine (SVM)
العنوانLightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation
النوعArticle
رقم العدد22
رقم المجلد23
dc.accessType Open Access


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