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Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network
Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential priva...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248995/ https://www.ncbi.nlm.nih.gov/pubmed/32365558 http://dx.doi.org/10.3390/s20092516 |
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author | Ju, Chunhua Gu, Qiuyang Wu, Gongxing Zhang, Shuangzhu |
author_facet | Ju, Chunhua Gu, Qiuyang Wu, Gongxing Zhang, Shuangzhu |
author_sort | Ju, Chunhua |
collection | PubMed |
description | Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user’s original data, and fundamentally protects the user’s data privacy. During this process, after receiving the data of the user’s local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility. |
format | Online Article Text |
id | pubmed-7248995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72489952020-06-10 Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network Ju, Chunhua Gu, Qiuyang Wu, Gongxing Zhang, Shuangzhu Sensors (Basel) Article Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user’s original data, and fundamentally protects the user’s data privacy. During this process, after receiving the data of the user’s local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility. MDPI 2020-04-29 /pmc/articles/PMC7248995/ /pubmed/32365558 http://dx.doi.org/10.3390/s20092516 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ju, Chunhua Gu, Qiuyang Wu, Gongxing Zhang, Shuangzhu Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network |
title | Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network |
title_full | Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network |
title_fullStr | Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network |
title_full_unstemmed | Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network |
title_short | Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network |
title_sort | local differential privacy protection of high-dimensional perceptual data by the refined bayes network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248995/ https://www.ncbi.nlm.nih.gov/pubmed/32365558 http://dx.doi.org/10.3390/s20092516 |
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