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Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network
This paper studies the privacy risk perception of online medical community users based on deep neural network. Firstly, this paper introduces privacy protection based on deep neural network and users’ privacy risk perception in online medical community. Then, using the fuzzy neural network to deal w...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240421/ https://www.ncbi.nlm.nih.gov/pubmed/35783711 http://dx.doi.org/10.3389/fpsyg.2022.914164 |
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author | Yin, Pei Zhang, Jun Yan, Han Zhao, Jun Wang, Jing Liang, Chunmei |
author_facet | Yin, Pei Zhang, Jun Yan, Han Zhao, Jun Wang, Jing Liang, Chunmei |
author_sort | Yin, Pei |
collection | PubMed |
description | This paper studies the privacy risk perception of online medical community users based on deep neural network. Firstly, this paper introduces privacy protection based on deep neural network and users’ privacy risk perception in online medical community. Then, using the fuzzy neural network to deal with highly complex and nonlinear data, we can better obtain the accurate evaluation value, and use the improved gravity search optimization algorithm to optimize the fuzzy neural network evaluation model and improve the convergence puzzle of the model. Finally, using the experimental method of questionnaire survey, and the questionnaire is composed of three parts. The first part investigates the basic personal information of the subjects, including gender, age, educational background, physical condition, physical examination frequency, Internet use experience, long-term residence, etc.; The second part is the measurement items of each variable in the theoretical model, including nine variables: service quality, personalized service, reciprocal norms, result expectation, material reward, perceived risk, trust in doctors, trust in websites, and willingness to disclose health privacy information. The experimental results show that the correlation coefficient between the interaction items of personalized service and reciprocal norms on material reward is positive (β = 0.072, P < 0.01), and the correlation coefficient between sexual service and material reward was positive (β = 0.202, P < 0.01), then reciprocal norms positively regulate the relationship between personalized service and material reward. |
format | Online Article Text |
id | pubmed-9240421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92404212022-06-30 Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network Yin, Pei Zhang, Jun Yan, Han Zhao, Jun Wang, Jing Liang, Chunmei Front Psychol Psychology This paper studies the privacy risk perception of online medical community users based on deep neural network. Firstly, this paper introduces privacy protection based on deep neural network and users’ privacy risk perception in online medical community. Then, using the fuzzy neural network to deal with highly complex and nonlinear data, we can better obtain the accurate evaluation value, and use the improved gravity search optimization algorithm to optimize the fuzzy neural network evaluation model and improve the convergence puzzle of the model. Finally, using the experimental method of questionnaire survey, and the questionnaire is composed of three parts. The first part investigates the basic personal information of the subjects, including gender, age, educational background, physical condition, physical examination frequency, Internet use experience, long-term residence, etc.; The second part is the measurement items of each variable in the theoretical model, including nine variables: service quality, personalized service, reciprocal norms, result expectation, material reward, perceived risk, trust in doctors, trust in websites, and willingness to disclose health privacy information. The experimental results show that the correlation coefficient between the interaction items of personalized service and reciprocal norms on material reward is positive (β = 0.072, P < 0.01), and the correlation coefficient between sexual service and material reward was positive (β = 0.202, P < 0.01), then reciprocal norms positively regulate the relationship between personalized service and material reward. Frontiers Media S.A. 2022-06-15 /pmc/articles/PMC9240421/ /pubmed/35783711 http://dx.doi.org/10.3389/fpsyg.2022.914164 Text en Copyright © 2022 Yin, Zhang, Yan, Zhao, Wang and Liang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Yin, Pei Zhang, Jun Yan, Han Zhao, Jun Wang, Jing Liang, Chunmei Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network |
title | Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network |
title_full | Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network |
title_fullStr | Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network |
title_full_unstemmed | Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network |
title_short | Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network |
title_sort | privacy risk perception of online medical community users based on deep neural network |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240421/ https://www.ncbi.nlm.nih.gov/pubmed/35783711 http://dx.doi.org/10.3389/fpsyg.2022.914164 |
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