A novel mechanism for fast detection of transformed data leakage

Data leakage is a growing insider threat in information security among organizations and individuals. A series of methods has been developed to address the problem of data leakage prevention (DLP). However, large amounts of unstructured data need to be tested in the big data era. As the volume of da...

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Main Authors: Huang, Xiaohong, Lu, Yunlong, Li, Dandan, Ma, Maode
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2018
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Online Access:https://hdl.handle.net/10356/87579
http://hdl.handle.net/10220/45443
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-875792020-03-07T13:57:31Z A novel mechanism for fast detection of transformed data leakage Huang, Xiaohong Lu, Yunlong Li, Dandan Ma, Maode School of Electrical and Electronic Engineering Data Leaks Weighted Graphs Data leakage is a growing insider threat in information security among organizations and individuals. A series of methods has been developed to address the problem of data leakage prevention (DLP). However, large amounts of unstructured data need to be tested in the big data era. As the volume of data grows dramatically and the forms of data become much complicated, it is a new challenge for DLP to deal with large amounts of transformed data. We propose an adaptive weighted graph walk model to solve this problem by mapping it to the dimension of weighted graphs. Our approach solves this problem in three steps. First, the adaptive weighted graphs are built to quantify the sensitivity of the tested data based on its context. Then, the improved label propagation is used to enhance the scalability for fresh data. Finally, a low-complexity score walk algorithm is proposed to determine the ultimate sensitivity. Experimental results show that the proposed method can detect leaks of transformed or fresh data fast and efficiently. Published version 2018-08-03T05:24:03Z 2019-12-06T16:44:53Z 2018-08-03T05:24:03Z 2019-12-06T16:44:53Z 2018 Journal Article Huang, X., Lu, Y., Li, D., & Ma, M. (2018). A novel mechanism for fast detection of transformed data leakage. IEEE Access, 6, 35926-35936. https://hdl.handle.net/10356/87579 http://hdl.handle.net/10220/45443 10.1109/ACCESS.2018.2851228 en IEEE Access © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 11 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Data Leaks
Weighted Graphs
spellingShingle Data Leaks
Weighted Graphs
Huang, Xiaohong
Lu, Yunlong
Li, Dandan
Ma, Maode
A novel mechanism for fast detection of transformed data leakage
description Data leakage is a growing insider threat in information security among organizations and individuals. A series of methods has been developed to address the problem of data leakage prevention (DLP). However, large amounts of unstructured data need to be tested in the big data era. As the volume of data grows dramatically and the forms of data become much complicated, it is a new challenge for DLP to deal with large amounts of transformed data. We propose an adaptive weighted graph walk model to solve this problem by mapping it to the dimension of weighted graphs. Our approach solves this problem in three steps. First, the adaptive weighted graphs are built to quantify the sensitivity of the tested data based on its context. Then, the improved label propagation is used to enhance the scalability for fresh data. Finally, a low-complexity score walk algorithm is proposed to determine the ultimate sensitivity. Experimental results show that the proposed method can detect leaks of transformed or fresh data fast and efficiently.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Huang, Xiaohong
Lu, Yunlong
Li, Dandan
Ma, Maode
format Article
author Huang, Xiaohong
Lu, Yunlong
Li, Dandan
Ma, Maode
author_sort Huang, Xiaohong
title A novel mechanism for fast detection of transformed data leakage
title_short A novel mechanism for fast detection of transformed data leakage
title_full A novel mechanism for fast detection of transformed data leakage
title_fullStr A novel mechanism for fast detection of transformed data leakage
title_full_unstemmed A novel mechanism for fast detection of transformed data leakage
title_sort novel mechanism for fast detection of transformed data leakage
publishDate 2018
url https://hdl.handle.net/10356/87579
http://hdl.handle.net/10220/45443
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