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Privacy-Preserving Sports Wearable Data Fusion Framework

When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is ba...

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Detalles Bibliográficos
Autores principales: Li, Jia, Zhang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095365/
https://www.ncbi.nlm.nih.gov/pubmed/35571697
http://dx.doi.org/10.1155/2022/6131971
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author Li, Jia
Zhang, Jie
author_facet Li, Jia
Zhang, Jie
author_sort Li, Jia
collection PubMed
description When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data.
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spelling pubmed-90953652022-05-12 Privacy-Preserving Sports Wearable Data Fusion Framework Li, Jia Zhang, Jie Comput Intell Neurosci Research Article When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data. Hindawi 2022-05-04 /pmc/articles/PMC9095365/ /pubmed/35571697 http://dx.doi.org/10.1155/2022/6131971 Text en Copyright © 2022 Jia Li and Jie Zhang. 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
Li, Jia
Zhang, Jie
Privacy-Preserving Sports Wearable Data Fusion Framework
title Privacy-Preserving Sports Wearable Data Fusion Framework
title_full Privacy-Preserving Sports Wearable Data Fusion Framework
title_fullStr Privacy-Preserving Sports Wearable Data Fusion Framework
title_full_unstemmed Privacy-Preserving Sports Wearable Data Fusion Framework
title_short Privacy-Preserving Sports Wearable Data Fusion Framework
title_sort privacy-preserving sports wearable data fusion framework
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095365/
https://www.ncbi.nlm.nih.gov/pubmed/35571697
http://dx.doi.org/10.1155/2022/6131971
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