Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

Abstract

Contrary to multimedia data watermarking approaches, it is not recommended that relational data watermarking techniques consider sequential selection for marks in the watermark and embedding locations in the digital asset being protected. Indeed, considering the database relations’ elements, i.e., tuple and attributes, when watermarking techniques are based on sequential processes, watermark detection can be easily compromised by performing subset reverse order attacks. As a result, attackers can obtain owner evidence-free high-quality data since no data modifications for mark removing are required for the malicious operation to succeed. A standard solution to this problem has been pseudo-random selection, which however often leads to choosing the same marks multiple times, ignoring others, thus compromising the embedding of the entire watermark. This work proposes an engine that contributes to control marks’ recurrent selection, allowing marks excluded by previous approaches to be considered. The experiments performed show a dramatic improvement of the embedded watermark quality when the proposed engine is included in watermarking techniques’ architecture. They also provide evidence that this proposal leads to higher resilience against common malicious operations such as subset and superset attacks.