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In this situation, quick integration and analysis of the medical records of patients in a cloud platform are of positive and valuable significance for accurate recognition and scientific diagnosis of the healthy conditions of potential patients. However, due to the big volume of medical data of patients distributed in different platforms (e.g., multiple hospitals), how to integrate these data for patient clustering and analysis in a time-efficient and scalable manner in cloud platform is still a challenging task, while guaranteeing the capability of privacy-preservation. Motivated by this fact, a time-efficient, scalable and privacy-guaranteed patient clustering method in cloud platform is proposed in this work. At last, we demonstrate the competitive advantages of our method via a set of simulated experiments. Experiment results with competitive methods in current research literatures have proved the feasibility of our proposal.<\/jats:p>","DOI":"10.1186\/s13677-022-00324-3","type":"journal-article","created":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T15:15:59Z","timestamp":1664032559000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient and scalable patients clustering based on medical big data in cloud platform"],"prefix":"10.1186","volume":"11","author":[{"given":"Yongsheng","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majid Ghani","family":"Varzaneh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"324_CR1","doi-asserted-by":"publisher","unstructured":"Kaixin Li, Jie Zhao, Jintao Hu, et al (2022) Dynamic Energy Efficient Task Offloading and Resource Allocation for NOMA-enabled IoT in Smart Buildings and Environment. 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