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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...

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Detalles Bibliográficos
Autores principales: Xu, Shasha, Yin, Xiufang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
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.
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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
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