Fast Detection of Transformed Data Leaks - 2016
The leak of sensitive data on computer systems poses a heavy threat to organizational security. Statistics show that the shortage of proper encryption on files and communications because of human errors is one of the leading causes of data loss. Organizations want tools to spot the exposure of sensitive information by screening the content in storage and transmission, i.e., to detect sensitive data being stored or transmitted in the clear. However, detecting the exposure of sensitive information is difficult because of data transformation in the content. Transformations (like insertion and deletion) end in highly unpredictable leak patterns. In this paper, we have a tendency to utilize sequence alignment techniques for detecting complex data-leak patterns. Our algorithm is designed for detecting long and inexact sensitive information patterns. This detection is paired with a comparable sampling algorithm, that allows one to check the similarity of two separately sampled sequences. Our system achieves sensible detection accuracy in recognizing remodeled leaks. We implement a parallelized version of our algorithms in graphics processing unit that achieves high analysis throughput. We tend to demonstrate the high multithreading scalability of our knowledge leak detection technique needed by a large organization.
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