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Privacy preserving dynamic data release against synonymous linkage based on microaggregation
The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837648/ https://www.ncbi.nlm.nih.gov/pubmed/35149712 http://dx.doi.org/10.1038/s41598-022-06182-y |
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author | Yan, Yan Eyeleko, Anselme Herman Mahmood, Adnan Li, Jing Dong, Zhuoyue Xu, Fei |
author_facet | Yan, Yan Eyeleko, Anselme Herman Mahmood, Adnan Li, Jing Dong, Zhuoyue Xu, Fei |
author_sort | Yan, Yan |
collection | PubMed |
description | The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods. |
format | Online Article Text |
id | pubmed-8837648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88376482022-02-14 Privacy preserving dynamic data release against synonymous linkage based on microaggregation Yan, Yan Eyeleko, Anselme Herman Mahmood, Adnan Li, Jing Dong, Zhuoyue Xu, Fei Sci Rep Article The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods. Nature Publishing Group UK 2022-02-11 /pmc/articles/PMC8837648/ /pubmed/35149712 http://dx.doi.org/10.1038/s41598-022-06182-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yan, Yan Eyeleko, Anselme Herman Mahmood, Adnan Li, Jing Dong, Zhuoyue Xu, Fei Privacy preserving dynamic data release against synonymous linkage based on microaggregation |
title | Privacy preserving dynamic data release against synonymous linkage based on microaggregation |
title_full | Privacy preserving dynamic data release against synonymous linkage based on microaggregation |
title_fullStr | Privacy preserving dynamic data release against synonymous linkage based on microaggregation |
title_full_unstemmed | Privacy preserving dynamic data release against synonymous linkage based on microaggregation |
title_short | Privacy preserving dynamic data release against synonymous linkage based on microaggregation |
title_sort | privacy preserving dynamic data release against synonymous linkage based on microaggregation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837648/ https://www.ncbi.nlm.nih.gov/pubmed/35149712 http://dx.doi.org/10.1038/s41598-022-06182-y |
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