A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

Early data leakage protection methods for smart mobile devices usually focus on confidential terms and their context, which truly prevent some kinds of data leakage events. However, with the high dimensionality and redundancy of text data, it is difficult to detect the documents which contain confid...

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Main Authors: Xiang Yu, Zhihong Tian, Jing Qiu, Feng Jiang
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2018/5823439
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spelling doaj-404bab340fa2491e9d50c2a1a98c5b8f2020-11-24T21:22:37ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772018-01-01201810.1155/2018/58234395823439A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile DevicesXiang Yu0Zhihong Tian1Jing Qiu2Feng Jiang3School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, ChinaCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, ChinaCollege of Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaEarly data leakage protection methods for smart mobile devices usually focus on confidential terms and their context, which truly prevent some kinds of data leakage events. However, with the high dimensionality and redundancy of text data, it is difficult to detect the documents which contain confidential contents accurately. Our approach updates cluster graph structure based on CBDLP (Data Leakage Protection Based on Context) model by computing the importance of confidential terms and the terms within the range of their context. By applying CBDLP with pruning procedure which has been validated, we further remove the redundancy terms and noise terms. Actually, not only can confidential terms be accurately detected but also the sophisticated rephrased confidential contents are detected during the experiments.http://dx.doi.org/10.1155/2018/5823439
collection DOAJ
language English
format Article
sources DOAJ
author Xiang Yu
Zhihong Tian
Jing Qiu
Feng Jiang
spellingShingle Xiang Yu
Zhihong Tian
Jing Qiu
Feng Jiang
A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices
Wireless Communications and Mobile Computing
author_facet Xiang Yu
Zhihong Tian
Jing Qiu
Feng Jiang
author_sort Xiang Yu
title A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices
title_short A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices
title_full A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices
title_fullStr A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices
title_full_unstemmed A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices
title_sort data leakage prevention method based on the reduction of confidential and context terms for smart mobile devices
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2018-01-01
description Early data leakage protection methods for smart mobile devices usually focus on confidential terms and their context, which truly prevent some kinds of data leakage events. However, with the high dimensionality and redundancy of text data, it is difficult to detect the documents which contain confidential contents accurately. Our approach updates cluster graph structure based on CBDLP (Data Leakage Protection Based on Context) model by computing the importance of confidential terms and the terms within the range of their context. By applying CBDLP with pruning procedure which has been validated, we further remove the redundancy terms and noise terms. Actually, not only can confidential terms be accurately detected but also the sophisticated rephrased confidential contents are detected during the experiments.
url http://dx.doi.org/10.1155/2018/5823439
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