Data-leakage detection scheme based on fingerprint and Bloom filters

doi:10.11772/j.issn.1001-9081.2014.07.1922

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 1922-1928.DOI: 10.11772/j.issn.1001-9081.2014.07.1922

Previous Articles Next Articles

Data-leakage detection scheme based on fingerprint and Bloom filters

HUANG Weiwen¹,LUO Jia²

1. Information Resource Center, Ningbo Polytechnic, Ningbo Zhejiang 315800, China;
2. School of Computer Science and Technology, Zhejiang University, Hangzhou Zhejiang 310058, China

Received:2014-01-20 Revised:2014-03-06 Online:2014-08-01 Published:2014-07-01
Contact: HUANG Weiwen

基于指纹和Bloom滤波器的数据泄漏检测方案

黄伟文¹,罗佳²

1. 宁波职业技术学院信息资源中心，浙江宁波 315800
2. 浙江大学计算机科学与技术学院，杭州 310058

通讯作者: 黄伟文
作者简介:黄伟文(1968-)，男，浙江宁波人，讲师，硕士，主要研究方向：算法设计、信息检索；罗佳(1983-)，女，浙江杭州人，博士研究生，主要研究方向:分布式计算、数据挖掘。
基金资助:
国家自然科学基金面上项目

Abstract

Abstract:

Aiming at the problems that the existing Data-Leakage Prevention (DLP) solutions are based on generic search for keywords in outgoing data, and hence severely lack the ability to control data flow at a fine granularity with low false probability. In this paper, an DLP architecture based on the white-listing was firstly designed, which used a white-listing for providing the strong security of data transmission. On this basis, a data leakage detection algorithm by combining document fingerprinting with Bloom filters was proposed. This algorithm computed the optimal locations by using dynamic programming to minimize the memory overhead and enable high-speed implementation. The simulation results show that the proposed algorithm for checking the fingerprints for a large amount of documents at very low cost. For example, for 1TB of documents, the proposed solution only requires 340MB memory to achieve worst case expected detection lag (i.e. leakage length) of 1000Bytes.

摘要：

针对当前的数据防泄漏方案主要依赖在外传数据中进行关键词通用搜索，导致数据流控制不够精细、虚警率较高的问题。首先设计了一种基于白名单的数据防泄漏(DLP)架构，在此基础上，提出了一种基于文件指纹和Bloom滤波器的数据泄露检测算法。该算法通过使用动态规划来计算最优检测位置，最大限度地降低了内存开销，并支持高速部署。仿真实验结果表明，所提算法可以用较低的代价，实现大量数据的在线指纹检测。例如，对1TB的文件，该方案只需340MB内存就可实现1000字节的最差检测延时期望(泄露的长度)。

CLC Number:

TP391

HUANG Weiwen LUO Jia. Data-leakage detection scheme based on fingerprint and Bloom filters[J]. Journal of Computer Applications, 2014, 34(7): 1922-1928.

黄伟文罗佳. 基于指纹和Bloom滤波器的数据泄漏检测方案[J]. 计算机应用, 2014, 34(7): 1922-1928.

References

［1］LIN C, SU W, MENG K,et al. Cloud computing security: architecture, mechanism and modeling［J］.Chinese Journal of Computers, 2013, 36(9): 1765-1784.(林闯, 苏文博, 孟坤, 等. 云计算安全: 架构, 机制与模型评价［J］. 计算机学报, 2013, 36(9):1765-1784.)
［2］LAWTON G. New technology prevents data leakage［J］. Computer, 2008, 41(9): 14-17.
［3］YOSHIHAMA S, MISHINA T, MATSUMOTO T. Web-based data leakage prevention［C］// Proceedings of the 5th International Workshop on Security. Piscataway: IEEE, 2010: 78-93.
［4］HONG C, ZHANG M, FENG D. Achieving efficient dynamic cryptographic access control in cloud storage［J］. Journal on Communications, 2011, 32(7): 125-131.(洪澄, 张敏, 冯登国. 面向云存储的高效动态密文访问控制方法［J］. 通信学报, 2011, 32(7): 125-131.)
［5］WANG Z, WANG Q. Practical control on sensitive data propagation of cloud computing［J］. China Information Security, 2013(8): 85-87.(王志文, 王强. 云计算敏感数据防泄露技术研究［J］. 信息安全与通信保密, 2013(8): 85-87.)
［6］CHEN J. Network security data transfer method research based on cloud computing［J］. Computer Simulation, 2012, 29(8): 139-141.(陈牮华. 基于云计算的网络安全数据传递方法研究［J］. 计算机仿真, 2012, 29(8): 139-141.)
［7］SCHEAR N, KINTANA C, ZHANG Q, et al. Glavlit: Preventing exfiltration at wire speed［J］. Irvine Is Burning, 2012,12(5): 133-140.
［8］YUMEREFENDI A R, MICKLE B, COX L P. TightLip: Keeping applications from spilling the beans［C］// Proceedings of the 9th USENIX Symposium on Networked Systems and Design and Implementation. Berkeley: USENIX Association, 2012: 289-296.
［9］KLONOWSKI M, KOZA M, KUTYLOWSKI M. Efficient and robust data aggregation using untrusted infrastructure［C］// Proceedings of the 6th International Conference on Security of Information and Networks. New York: ACM, 2013: 123-130.
［10］MITZENMACHER M. Compressed bloom filters［J］. IEEE/ACM Transactions on Networking, 2002, 10(5): 604-612.
［11］HAO F, KODIALAM M, LAKSHMAN T V. Building high accuracy bloom filters using partitioned hashing［J］. ACM SIGMETRICS Performance Evaluation Review, 2007, 35(1): 277-288.
［12］PUTTASWAMY K P N, SALA A, ZHAO B Y. Starclique: Guaranteeing user privacy in social networks against intersection attacks［C］// Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies. New York: ACM, 2009: 157-168.
［13］PUTTASWAMY K P N, MARSHALL C C, RAMASUBRAMANIAN V, et al. Docx2Go: collaborative editing of fidelity reduced documents on mobile devices［C］// Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. New York: ACM, 2010: 345-356.

[1]	. Pavement defect detection algorithm with enhanced morphological perception [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	YANG Jianfeng, CHEN Bin, LI Yuxuan. Self-supervised point cloud anomaly detection method based on point cloud reconstruction [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	XUE Zhenhua, LI Qiang, HUANG Chao. Vision foundation model-driven pixel-level image anomaly detection model [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Binocular vision object location algorithm for robot arm grasping [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Multi-attention contrastive learning for infrared small target detection [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	LI Zhonghua, ZHONG Gengxin, FAN Ping, ZHU Hengliang. Camouflaged object detection by boundary mining and background guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[7]	. Multi-view difficult airway recognition based on discriminant region guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[8]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	. Gravity data denoising method based on multilevel wavelet residual network#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Data augmentation method for abnormal passenger behavior in elevators based on dynamic graph convolutional network [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Text-based person retrieval method based on multi-granularity shared semantic center association [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	ZHANG Qing, YANG Fan, FANG Yuhan. Chinese Spelling Correction Algorithm Based on Multi-Modal Information Fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	WANG Hao, WANG Jinwei, CHENG Xin, ZHANG Jiawei, WU Hao, LUO Xiangyang, MA Bin. A Review of JPEG Recompression Forensics for Color Images [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Distribution adaptation and dynamic curriculum pseudo-labeling framework for semi-supervised fire detection [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Multimodal harmful content detection method based on weakly supervised modality semantic enhancement [J]. Journal of Computer Applications, 0, (): 0-0.

Data-leakage detection scheme based on fingerprint and Bloom filters

基于指纹和Bloom滤波器的数据泄漏检测方案

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics