An Attribute-attack-proof Watermarking Technique for Relational Database

Proving ownership rights on relational databases is an important issue. The robust watermarking technique could claim ownership by insertion information about the data owner. Hence, it is vital to improving the robustness of watermarking technique in that intruders could launch types of attacks to corrupt the inserted watermark. Furthermore, attributes are explicit and operable objectives to destroy the watermark. To my knowledge, there does not exist a comprehensive solution to resist attribute attack. In this paper, we propose a robust watermarking technique that is robust against subset and attribute attacks. The novelties lie in several points: applying the classifier to reorder watermarked attributes, designing a secret sharing mechanism to duplicate watermark independently on each attribute, and proposing twice majority voting to correct errors caused by attacks for improving the accuracy of watermark detection. In addition, our technique has features of blind, key-based, incrementally updatable, and low false hit rate. Experiments show that our algorithm is robust against subset and attribute attacks compared with AHK, DEW, and KSF algorithms. Moreover, it is efficient with running time in both insertion and detection phases.