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Recommendation System for Privacy-Preserving Education Technologies
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9034935/ https://www.ncbi.nlm.nih.gov/pubmed/35469207 http://dx.doi.org/10.1155/2022/3502992 |
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author | Xu, Shasha Yin, Xiufang |
author_facet | Xu, Shasha Yin, Xiufang |
author_sort | Xu, Shasha |
collection | PubMed |
description | Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals' personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users' privacy. |
format | Online Article Text |
id | pubmed-9034935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90349352022-04-24 Recommendation System for Privacy-Preserving Education Technologies Xu, Shasha Yin, Xiufang Comput Intell Neurosci Research Article Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals' personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users' privacy. Hindawi 2022-04-16 /pmc/articles/PMC9034935/ /pubmed/35469207 http://dx.doi.org/10.1155/2022/3502992 Text en Copyright © 2022 Shasha Xu and Xiufang Yin. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Xu, Shasha Yin, Xiufang Recommendation System for Privacy-Preserving Education Technologies |
title | Recommendation System for Privacy-Preserving Education Technologies |
title_full | Recommendation System for Privacy-Preserving Education Technologies |
title_fullStr | Recommendation System for Privacy-Preserving Education Technologies |
title_full_unstemmed | Recommendation System for Privacy-Preserving Education Technologies |
title_short | Recommendation System for Privacy-Preserving Education Technologies |
title_sort | recommendation system for privacy-preserving education technologies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9034935/ https://www.ncbi.nlm.nih.gov/pubmed/35469207 http://dx.doi.org/10.1155/2022/3502992 |
work_keys_str_mv | AT xushasha recommendationsystemforprivacypreservingeducationtechnologies AT yinxiufang recommendationsystemforprivacypreservingeducationtechnologies |