In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack.

Semantic-driven watermarking of relational textual databases

Perez Gort M. L.;Olliaro M.;Cortesi A.;
2021-01-01

Abstract

In relational database watermarking, the semantic consistency between the original database and the distorted one is a challenging issue which is disregarded by most watermarking proposals, due to the well-known assumption for which a small amount of errors in the watermarked database is tolerable. We propose a semantic-driven watermarking approach of relational textual databases, which marks multi-word textual attributes, exploiting the synonym substitution technique for text watermarking together with notions in semantic similarity analysis, and dealing with the semantic perturbations provoked by the watermark embedding. We show the effectiveness of our approach through an experimental evaluation, highlighting the resulting capacity, robustness and imperceptibility watermarking requirements. We also prove the resilience of our approach with respect to the random synonym substitution attack.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3734700
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