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Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment
With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. However, the past sports training records...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9517989/ https://www.ncbi.nlm.nih.gov/pubmed/36193237 http://dx.doi.org/10.1186/s13677-022-00325-2 |
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author | Diao, Guoyan Liu, Fang Zuo, Zhikai Moghimi, Mohammad Kazem |
author_facet | Diao, Guoyan Liu, Fang Zuo, Zhikai Moghimi, Mohammad Kazem |
author_sort | Diao, Guoyan |
collection | PubMed |
description | With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. 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 privacy-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’ 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. |
format | Online Article Text |
id | pubmed-9517989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-95179892022-09-29 Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment Diao, Guoyan Liu, Fang Zuo, Zhikai Moghimi, Mohammad Kazem J Cloud Comput (Heidelb) Research With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. 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 privacy-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’ 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. Springer Berlin Heidelberg 2022-09-28 2022 /pmc/articles/PMC9517989/ /pubmed/36193237 http://dx.doi.org/10.1186/s13677-022-00325-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Diao, Guoyan Liu, Fang Zuo, Zhikai Moghimi, Mohammad Kazem Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment |
title | Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment |
title_full | Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment |
title_fullStr | Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment |
title_full_unstemmed | Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment |
title_short | Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment |
title_sort | privacy-aware and efficient student clustering for sport training with hash in cloud environment |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9517989/ https://www.ncbi.nlm.nih.gov/pubmed/36193237 http://dx.doi.org/10.1186/s13677-022-00325-2 |
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