{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:55:08Z","timestamp":1775145308841,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Saudi Arabia","award":["RSPD2023R636"],"award-info":[{"award-number":["RSPD2023R636"]}]},{"name":"King Saud University, Saudi Arabia","award":["13-0205-200270"],"award-info":[{"award-number":["13-0205-200270"]}]},{"name":"Qatar National Research Fund (QNRF), Qatar","award":["RSPD2023R636"],"award-info":[{"award-number":["RSPD2023R636"]}]},{"name":"Qatar National Research Fund (QNRF), Qatar","award":["13-0205-200270"],"award-info":[{"award-number":["13-0205-200270"]}]},{"name":"Qatar National Library","award":["RSPD2023R636"],"award-info":[{"award-number":["RSPD2023R636"]}]},{"name":"Qatar National Library","award":["13-0205-200270"],"award-info":[{"award-number":["13-0205-200270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/s23229033","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T01:54:30Z","timestamp":1699408470000},"page":"9033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5021-3950","authenticated-orcid":false,"given":"Sherif","family":"Abdelfattah","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5153-8693","authenticated-orcid":false,"given":"Mohamed","family":"Baza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Charleston, Charleston, SC 29424, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8719-501X","authenticated-orcid":false,"given":"Mohamed","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-8640","authenticated-orcid":false,"given":"Mostafa M.","family":"Fouda","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA"},{"name":"Center for Advanced Energy Studies (CAES), Idaho Falls, ID 83401, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6693-3386","authenticated-orcid":false,"given":"Khalid","family":"Abualsaud","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar"}]},{"given":"Elias","family":"Yaacoub","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8601-3184","authenticated-orcid":false,"given":"Maazen","family":"Alsabaan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi P.O. Box 131818, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88012","DOI":"10.1109\/ACCESS.2019.2925625","article-title":"Flexible and efficient blockchain-based ABE scheme with multi-authority for medical on demand in telemedicine system","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Teladochealth (2023, September 15). Teladoc Health. Available online: https:\/\/www.teladochealth.com\/."},{"key":"ref_3","unstructured":"Google (2023, September 15). Google Machine Learning Project. 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