基于模拟退火和粒子群混合改进算法的数据库水印技术

doi:10.3969/j.issn.1671-1122.2022.05.005

信息网络安全 ›› 2022, Vol. 22 ›› Issue (5): 37-45.doi: 10.3969/j.issn.1671-1122.2022.05.005

基于模拟退火和粒子群混合改进算法的数据库水印技术

孔嘉琪¹^,², 王利明¹(), 葛晓雪¹^,²

1.中国科学院信息工程研究所,北京 100093
2.中国科学院大学网络空间安全学院,北京 100049

收稿日期:2022-02-10 出版日期:2022-05-10 发布日期:2022-06-02
通讯作者: 王利明 E-mail:wangliming@iie.ac.cn
作者简介:孔嘉琪（1997—）,女,山西,硕士研究生,主要研究方向为数据安全|王利明（1978—）,男,北京,正高级工程师,博士,主要研究方向为云计算安全、网络安全、大数据安全、5G安全、区块链安全|葛晓雪（1997—）,女,河北,硕士研究生,主要研究方向为数据安全
基金资助:
国家重点研发计划(2017YFB0801900)

Simulated Annealing and Particle Swarm Enhanced Relational Database Watermark

KONG Jiaqi¹^,², WANG Liming¹(), GE Xiaoxue¹^,²

1. Institute of Information Engineering, University of Chinese Academy of Sciences, Beijing 100093, China
2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-02-10 Online:2022-05-10 Published:2022-06-02
Contact: WANG Liming E-mail:wangliming@iie.ac.cn

摘要/Abstract

摘要：

近年来,随着数据开放程度的提升,数据库水印成为了一种越来越重要的安全手段。数据库水印可以对泄露数据进行版权认证和溯源追责,保障数据的安全。现有的数据库水印嵌入方案存在水印容量低、抗攻击的鲁棒性较弱等问题,无法对数据进行有效保护。文章提出了PADEW数据库水印方案,PADEW使用基于模拟退火改进的粒子群算法寻找更好的水印嵌入位置,以解决一些现有方案容易陷入局部最优解的问题,从而提高水印嵌入容量并降低水印嵌入引起的失真。PADEW提出了基于属性重要度的带权损失函数,以解决现有方案在抗属性维度攻击时鲁棒性较弱的问题。文章使用水印嵌入容量、平均失真和面对多种攻击时的水印检测率评估PADEW的性能,并和现有的一些方案进行对比。实验表明,PADEW能在提高水印容量的同时,降低水印嵌入引起的失真。并且,PADEW有较强的抗攻击的鲁棒性,包括元组删除攻击、元组添加攻击、比特翻转攻击和属性删除攻击。尤其在面对50%的属性删除攻击时,水印检测率仍然高达81%。

关键词: 数据库水印, 模拟退火算法, 粒子群算法, 水印容量, 抗攻击

Abstract:

In recent years, with the improvement of data openness, database watermark has become increasingly important in database security. Database watermark can carry out copyright authentication and traceability of leaked data to ensure data security. However, existing watermark models have low watermark capacity and weak anti-attack robustness. This paper proposes a new database watermark method PADEW. PADEW used an improved particle swarm algorithm based on simulated annealing to avoid falling into the local optimal solution. This enhancement found better watermark embedding positions, thereby increased the watermark capacity and reducing distortion. In addition, this research proposed to use a weighted loss function based on attribute importance to improve the robustness against attribute dimension attacks. The experiments employed watermark capacity, average distortion, and watermark detection rate against multiple attacks to evaluate the performance of PADEW. Experiment results show that PADEW can reduce the distortion caused by watermark embedding while providing more watermark capacity. In addition, PADEW has stronger robustness against various attacks, including tuple deletion attacks, tuple addition attacks, bit flip attacks, and attribute deletion attacks. Especially in the face of 50% attribute deletion attacks, the watermark detection rate is still as high as 81%.

Key words: database watermark, simulated annealing algorithm, particle swarm algorithm, watermark capacity, anti-attack

中图分类号:

TP309

孔嘉琪, 王利明, 葛晓雪. 基于模拟退火和粒子群混合改进算法的数据库水印技术[J]. 信息网络安全, 2022, 22(5): 37-45.

KONG Jiaqi, WANG Liming, GE Xiaoxue. Simulated Annealing and Particle Swarm Enhanced Relational Database Watermark[J]. Netinfo Security, 2022, 22(5): 37-45.

图/表 7

图1

表1

表2

图2

图3

图4

图5

参考文献 19

[1]	Canalys. Cybersecurity Investment 2020[EB/OL]. [2022-04-12]. https://www.canalys.com/reports/cybersecurity-market.
[2]	AGRAWAL R, KIERNAN J. Watermarking Relational Databases[C]// VLDB. Proceedings of 28th International Conference on Very Large Data Bases. San Fransisco: Morgan Kaufmann, 2002: 155-166.
[3]	GUO Fei, WANG Jianmin, LI Deyi. Fingerprinting Relational Databases[C]// SAC. Proceedings of the 2006 ACM Symposium on Applied computing. New York: ACM, 2006: 487-492.
[4]	GUPTA G, PIEPRZYK J. Reversible and Blind Database Watermarking Using Difference Expansion[C]// e-Forensics. Proceedings of the 1st International Conference on Forensic Applications and Techniques in Telecommunications, Information, and Multimedia and Workshop. Brussels, BEL: ICST, 2008: 6-14.
[5]	CHAI Heyan, YANG Shuqiang. A Robust and Reversible Watermarking Technique for Relational Dataset Based on Clustering[C]// TrustCom/BigDataSE. 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering. New Zealand: IEEE, 2019: 411-418.
[6]	JAWAD K, KHAN A. Genetic Algorithm and Difference Expansion Based Reversible Watermarking for Relational Databases[J]. Journal of Systems and Software, 2013, 86(11): 2742-2753. doi: 10.1016/j.jss.2013.06.023 URL
[7]	HU Donghui, ZHAO Dan, ZHENG Shuli. A New Robust Approach for Reversible Database Watermarking with Distortion Control[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(6): 1024-1037. doi: 10.1109/TKDE.2018.2851517 URL
[8]	PARSOPOULOS K E, VRAHATIS M N. Particle Swarm Optimization Method for Constrained Optimization Problems[J]. Intelligent Technologies-Theory and Application: New Trends in Intelligent Technologies, 2002, 76(1): 214-220.
[9]	KAMRAN M, FAROOQ M. An Information-Preserving Watermarking Scheme for Right Protection of EMR Systems[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 24(11): 1950-1962. doi: 10.1109/TKDE.2011.223 URL
[10]	KAMRAN M, SUHAIL S, FAROOQ M. A Robust, Distortion Minimizing Technique for Watermarking Relational Databases Using Once-for-All Usability Constraints[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2694-2707. doi: 10.1109/TKDE.2012.227 URL
[11]	SHEHAB M, BERTINO E, GHAFOOR A. Watermarking Relational Databases Using Optimization-Based Techniques[J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 20(1): 116-129. doi: 10.1109/TKDE.2007.190668 URL
[12]	UNNIKRISHNAN K, PRAMOD K V. Robust Aptimal Position Detection Scheme for Relational Database Watermarking Through HOLPSOFA Algorithm[J]. Journal of Information Security& Applications, 2017, 35(8): 1-12.
[13]	IFTIKHAR S, KAMRAN M. RRW-A Robust and Reversible Watermarking Technique for Relational Data[J]. IEEE Transactions on Knowledge& Data Engineering, 2014, 27(4): 1132-1145.
[14]	MACQUEEN J. Some Methods for Classification and Analysis of MultiVariate Observations[C]// Berkeley. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. USA: University of California Press, 1967:281-297.
[15]	KENNEDY J, EBERHART R. Particle Swarm Optimization[C]// IEEE. Proceedings of ICNN'95-International Conference on Neural Networks. New York: IEEE, 1995: 1942-1948.
[16]	METROPOLIS N, ROSENBLUTH A W, ROSENBLUTH M N, et al. Equation of State Calculations by Fast Computing Machines[J]. The Journal of Chemical Physics, 1953, 21(6): 1087-1092. doi: 10.1063/1.1699114 URL
[17]	LIU Aijun, YANG Yu, LI Fei, et al. Chaotic Simulated Annealing Particle Swarm Optimization Algorithm Research and Its Application[J]. Journal of Zhejiang University(Engineering Science), 2013, 47(10): 1722-1730.
	刘爱军, 杨育, 李斐, 等. 混沌模拟退火粒子群优化算法研究及应用[J]. 浙江大学学报(工学版), 2013, 47(10): 1722-1730.
[18]	WANG Huaqiu, CAO Changxiu. Parallel Particle Swarm Optimization Based on Simulated Annealing[J]. Control and Decision, 2005(5): 500-504.
	王华秋, 曹长修. 基于模拟退火的并行粒子群优化研究[J]. 控制与决策, 2005(5):500-504.
[19]	ZHAO Xinye, TANG Shuai, YANG Mei, et al. Hybrid Algorithm Based on Particle Swarm Optimization and Simulated in Timed Influence Nets[J]. Computer Science, 2012, 39(Z3): 63-66.
	赵鑫业, 唐帅, 杨妹, 等. 基于赋时影响网的模拟退火与粒子群混合改进算法[J]. 计算机科学, 2012, 39(Z3):63-66.

编辑推荐 0

Metrics

阅读次数

全文

205

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	8	0	0	197

来源	本网站	其他网站

次数	205	0
比例	100%	0%

摘要

284

最新录用	在线预览	正式出版

0	0	284

	来源	本网站

	次数	284
	比例	100%

元组数算法	255	1561	3551	5013	6779	11259	均值
PADEW	43.0%	44.9%	44.0%	45.4%	43.0%	45.8%	44.4%
PADNF	37.6%	44.8%	44.0%	44.2%	44.0%	43.7%	43.1%
PSODEW	20.0%	43.0%	42.0%	42.3%	37.8%	30.5%	35.9%
DEW	21.0%	34.9%	36%	32.5%	32.1%	27.6%	30.7%

元组数算法	255	1561	3551	5013	6779	11259	均值
PADEW	30.7	41.6	45.9	43.8	37.6	42.9	40.4
PADNF	30.6	42.7	39.4	43.6	39.9	39.6	39.3
PSODEW	59.1	41.8	41.1	42.7	47.9	45.3	46.3
DEW	61	57.3	47.8	47.9	49.9	62.5	54.4

基于模拟退火和粒子群混合改进算法的数据库水印技术

Simulated Annealing and Particle Swarm Enhanced Relational Database Watermark

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 19

相关文章 5

编辑推荐 0

Metrics

本文评价

[1]	宋玉龙, 王磊, 武欣嵘, 曾维军. 基于模拟退火自适应粒子群算法的WSN拓扑抗毁性方法研究[J]. 信息网络安全, 2021, 21(6): 89-96.
[2]	王文华, 郝新, 刘焱, 王洋. AI系统的安全测评和防御加固方案[J]. 信息网络安全, 2020, 20(9): 87-91.
[3]	余晴, 郑崇辉, 杜晔. 面向云平台虚拟层的安全态势评估关键技术研究[J]. 信息网络安全, 2020, 20(7): 53-59.
[4]	仲明, 贾徽徽, 姜丽莹, 王潮. 基于量子退火算法的DPA防御系统优化[J]. 信息网络安全, 2016, 16(3): 28-33.
[5]	李凤海;张佰龙;刘皖;陈长敏. 小型专用电子设备抗攻击检测算法研究[J]. , 2013, 13(10): 0-0.