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However, the past sports training records of students are often accumulated with time and stored in a central cloud platform and therefore, the data volume is too large to be processed with quick response. In addition, the past sports training records of students often contain certain sensitive information, which probably discloses partial user privacy if we cannot protect the data well. Considering these two challenges, a <jats:underline>p<\/jats:underline>rivacy-aware and efficient student clustering approach, named PESC is proposed, which is based on a hash technique and deployed on a central cloud platform connecting multiple local servers. Concretely, in the cloud platform, each student is firstly assigned an index based on the past sports training records stored in a local server, through a uniform hash mapping operation. Then similar students are clustered and registered in the cloud platform based on the students\u2019 respective sport indexes. At last, we infer the personalized sport preferences of each student based on their belonged clusters. To prove the feasibility of PESC, we provide a case study and a set of experiments deployed on a time-aware dataset.<\/jats:p>","DOI":"10.1186\/s13677-022-00325-2","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T16:05:10Z","timestamp":1664381110000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment"],"prefix":"10.1186","volume":"11","author":[{"given":"Guoyan","family":"Diao","sequence":"first","affiliation":[]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhikai","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"Mohammad Kazem","family":"Moghimi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"325_CR1","doi-asserted-by":"publisher","unstructured":"Li K, Zhao J, Hu J, et al. 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