Rights Protection for Relational Data
IEEE Transactions on Knowledge and Data Engineering
Genetic algorithms based approach to database vertical partition
Journal of Intelligent Information Systems
Watermarking Relational Databases Using Optimization-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
Reversible and blind database watermarking using difference expansion
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Machine learning based adaptive watermark decoding in view of anticipated attack
Pattern Recognition
Adaptive watermark mechanism for rightful ownership protection
Journal of Systems and Software
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
Real-time task scheduling by multiobjective genetic algorithm
Journal of Systems and Software
A high capacity reversible data hiding scheme with edge prediction and difference expansion
Journal of Systems and Software
A blind reversible method for watermarking relational databases based on a time-stamping protocol
Expert Systems with Applications: An International Journal
Intelligent reversible watermarking in integer wavelet domain for medical images
Journal of Systems and Software
Reversible watermark using the difference expansion of a generalized integer transform
IEEE Transactions on Image Processing
Reversible data embedding using a difference expansion
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present a new robust and reversible watermarking approach for the protection of relational databases. Our approach is based on the idea of difference expansion and utilizes genetic algorithm (GA) to improve watermark capacity and reduce distortion. The proposed approach is reversible and therefore, distortion introduced after watermark insertion can be fully restored. Using GA, different attributes are explored to meet the optimal criteria rather than selecting less effective attributes for watermark insertion. Checking only the distortion tolerance of two attributes for a selected tuple may not be useful for watermark capacity and distortion therefore, distortion tolerance of different attributes are explored. Distortion caused by difference expansion can help an attacker to predict watermarked attribute. Thus, we have incorporated tuple and attribute-wise distortion in the fitness function of GA, making it tough for an attacker to predict watermarked attribute. From experimental analysis, it is concluded that the proposed technique provides improved capacity and reduced distortion compared to existing approaches. Problem of false positives and change in attribute order at detection side is also resolved. Additionally, the proposed technique is resilient against a wide range of attacks such as addition, deletion, sorting, bit flipping, tuple-wise-multifaceted, attribute-wise-multifaceted, and additive attacks.