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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...
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/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. |
format | Online Article Text |
id | pubmed-9095365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lijia privacypreservingsportswearabledatafusionframework AT zhangjie privacypreservingsportswearabledatafusionframework |