Fast Detection of Transformed Data Leaks

—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, deletion) result in highly unpredictable leak patterns. In this work, we utilize sequence alignment techniques for detecting complex data-leak patterns. Our 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. Our system achieves good ...

• Type: Journal
• Year: 2016
• Where: TIFS
• Authors: Xiaokui Shu, Jing Zhang, Danfeng (Daphne) Yao, Wu-chun Feng
• Keywords: TIFS 2016