基于统计量特征的数据库指纹方法  

Database Fingerprinting Based on Statistical Features

作　　者：方焱志 黄煜坤 彭煜玮[1] FANG Yan-Zhi;HUANG Yu-Kun;PENG Yu-Wei(School of Computer Science,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学计算机学院,湖北武汉430072

出　　处：《软件学报》2022年第9期3422-3436,共15页Journal of Software

摘　　要：在数据库中嵌入数字水印作为指纹是一种重要的数据库版权保护与身份溯源技术,它对推动数据的共享融合起到了重要的保障作用.针对现有数据库指纹方法在数据普适性上的不足开展研究,提出了一种基于统计量特征的数据库指纹方法.首先,采用了迭代哈希的数据划分方法将数据划分为多个子集;然后,通过最优化算法将过滤极值后的数据子集特征最大/最小化,根据基于最小错误率的贝叶斯决策计算得到最优阈值作为指纹信息.通过理论分析验证了该方法的可行性与有效性,同时也通过真实数据集上的实验结果证明了所提算法在抗攻击能力和普适性上的优势.Digital watermarking to form fingerprints in databases is an important approach for database right protection and ownership identification. It provides protection for the sharing and fusion of data. As existing database fingerprinting methods have a deficiency in the universality of data, this study proposes a database fingerprinting approach based on statistical features. This approach first divides the host data into several subsets by an iterative hash function. Then, the statistical feature of each subset is maximized/minimized by an optimization algorithm after extreme values are filtered out. Finally, the optimum threshold is taken as fingerprint information which is calculated by Bayesian decision for minimum errors. This study also theoretically verifies the feasibility and effectiveness of the proposed method. The experimental results on real datasets demonstrate that the method has advantages in both robustness and universality.

关 键 词：数字水印 数据库指纹 统计量特征 可用性约束 鲁棒性水印 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]