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Local differential privacy protection for wearable device data
Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users’ personal privacy, a collection scheme based on local differential...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385068/ https://www.ncbi.nlm.nih.gov/pubmed/35976869 http://dx.doi.org/10.1371/journal.pone.0272766 |
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author | Li, Zhangbing Wang, Baichuan Li, Jinsheng Hua, Yi Zhang, Shaobo |
author_facet | Li, Zhangbing Wang, Baichuan Li, Jinsheng Hua, Yi Zhang, Shaobo |
author_sort | Li, Zhangbing |
collection | PubMed |
description | Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users’ personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved. |
format | Online Article Text |
id | pubmed-9385068 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93850682022-08-18 Local differential privacy protection for wearable device data Li, Zhangbing Wang, Baichuan Li, Jinsheng Hua, Yi Zhang, Shaobo PLoS One Research Article Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users’ personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved. Public Library of Science 2022-08-17 /pmc/articles/PMC9385068/ /pubmed/35976869 http://dx.doi.org/10.1371/journal.pone.0272766 Text en © 2022 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Zhangbing Wang, Baichuan Li, Jinsheng Hua, Yi Zhang, Shaobo Local differential privacy protection for wearable device data |
title | Local differential privacy protection for wearable device data |
title_full | Local differential privacy protection for wearable device data |
title_fullStr | Local differential privacy protection for wearable device data |
title_full_unstemmed | Local differential privacy protection for wearable device data |
title_short | Local differential privacy protection for wearable device data |
title_sort | local differential privacy protection for wearable device data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385068/ https://www.ncbi.nlm.nih.gov/pubmed/35976869 http://dx.doi.org/10.1371/journal.pone.0272766 |
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