Paper Info
Title
Fast Detection of Transformed Data Leaks
Abstract
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 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 detection accuracy in recognizing transformed leaks. We implement a parallelized version of our algorithms in graphics processing unit that achieves high analysis throughput. We demonstrate the high multithreading scalability of our data leak detection method required by a sizable organization.
Year
DOI
Venue
2016
10.1109/TIFS.2015.2503271
Information Forensics and Security, IEEE Transactions
Keywords
Field
DocType
Data leak detection,alignment,content inspection,dynamic programming,parallelism,sampling
Multithreading,Deep content inspection,Algorithm design,Data loss,Computer science,Encryption,Real-time computing,Graphics processing unit,Pattern matching,Scalability
Journal
Volume
Issue
ISSN
11
3
1556-6013
Citations 
PageRank 
References 
11
0.65
42
Authors
4
Name
Order
Citations
PageRank
Xiaokui Shu1997.47
Jing Zhang2706.53
Danfeng Yao396574.85
Wu-chun Feng42812232.50