A Novel Mechanism for Fast Detection of Transformed Data Leakage

Abstract

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.