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An efficient reversible privacy-preserving data mining technology over data streams
With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and use...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995193/ https://www.ncbi.nlm.nih.gov/pubmed/27610326 http://dx.doi.org/10.1186/s40064-016-3095-3 |
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author | Lin, Chen-Yi Kao, Yuan-Hung Lee, Wei-Bin Chen, Rong-Chang |
author_facet | Lin, Chen-Yi Kao, Yuan-Hung Lee, Wei-Bin Chen, Rong-Chang |
author_sort | Lin, Chen-Yi |
collection | PubMed |
description | With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and uses the concept of a sliding window to design a reversible privacy-preserving technology to process continuous data in real time, known as a continuous reversible privacy-preserving (CRP) algorithm. Data with CRP algorithm protection can be accurately recovered through a data recovery process. In addition, by using an embedded watermark, the integrity of the data can be verified. The results from the experiments show that, compared to existing algorithms, CRP is better at preserving knowledge and is more effective in terms of reducing information loss and privacy disclosure risk. In addition, it takes far less time for CRP to process continuous data than existing algorithms. As a result, CRP is confirmed as suitable for data stream environments and fulfills the requirements of being lightweight and energy-efficient for smart handheld devices. |
format | Online Article Text |
id | pubmed-4995193 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49951932016-09-08 An efficient reversible privacy-preserving data mining technology over data streams Lin, Chen-Yi Kao, Yuan-Hung Lee, Wei-Bin Chen, Rong-Chang Springerplus Research With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and uses the concept of a sliding window to design a reversible privacy-preserving technology to process continuous data in real time, known as a continuous reversible privacy-preserving (CRP) algorithm. Data with CRP algorithm protection can be accurately recovered through a data recovery process. In addition, by using an embedded watermark, the integrity of the data can be verified. The results from the experiments show that, compared to existing algorithms, CRP is better at preserving knowledge and is more effective in terms of reducing information loss and privacy disclosure risk. In addition, it takes far less time for CRP to process continuous data than existing algorithms. As a result, CRP is confirmed as suitable for data stream environments and fulfills the requirements of being lightweight and energy-efficient for smart handheld devices. Springer International Publishing 2016-08-24 /pmc/articles/PMC4995193/ /pubmed/27610326 http://dx.doi.org/10.1186/s40064-016-3095-3 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Lin, Chen-Yi Kao, Yuan-Hung Lee, Wei-Bin Chen, Rong-Chang An efficient reversible privacy-preserving data mining technology over data streams |
title | An efficient reversible privacy-preserving data mining technology over data streams |
title_full | An efficient reversible privacy-preserving data mining technology over data streams |
title_fullStr | An efficient reversible privacy-preserving data mining technology over data streams |
title_full_unstemmed | An efficient reversible privacy-preserving data mining technology over data streams |
title_short | An efficient reversible privacy-preserving data mining technology over data streams |
title_sort | efficient reversible privacy-preserving data mining technology over data streams |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995193/ https://www.ncbi.nlm.nih.gov/pubmed/27610326 http://dx.doi.org/10.1186/s40064-016-3095-3 |
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