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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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 |
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Data Leaks Weighted Graphs Huang, Xiaohong Lu, Yunlong Li, Dandan Ma, Maode A novel mechanism for fast detection of transformed data leakage |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Huang, Xiaohong Lu, Yunlong Li, Dandan Ma, Maode |
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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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1681046075062878208 |