Fast data leakage, content inspection, sampling, alignment, parallelism
Surveys from many years have shown that many data leakages has been found due different problems like malicious attacks, hacking, different attacks. The leak of sensitive data on computer systems poses a serious threat to organizational security. Statistics show that the lack of proper encryption on files and communications due to human errors is one of the leading causes of data loss. Organizations need tools to identify the exposure of sensitive data by screening the content in storage and transmission, i.e., to detect sensitive information being stored or transmitted in the clear. However, detecting the exposure of sensitive information is challenging due to data transformation in the content. Transformations (such as insertion and deletion) result in highly unpredictable leak patterns. In this paper, we utilize sequence alignment techniques for detectin complex data-leak patterns.There algorithm is designed for detecting long and inexact sensitive data patterns.
This detection is paired with a comparable sampling algorithm, which allows one to compare the similarity of two separately sampled sequences. There system achieves good detection accuracy in recognizing transformed leaks. The implement a parallelized version of an algorithms in graphics processing unit that achieves high analysis throughput.