Robust Watermarking of Relational Databases With Ontology-Guided Distortion Control
In this paper, we present a new robust database watermarking scheme the originality of which stands on a semantic control of the data distortion and on the extension of quantization index modulation (QIM) to circular histograms of numerical attributes. The semantic distortion control of the embedding process we propose relies on the identification of existing semantic links in between values of attributes in a tuple by means of an ontology. By doing so, we avoid incoherent or very rare record occurrences which may bias data interpretation or betray the presence of the watermark. In a second time, we adapt QIM to database watermarking. Watermark embedding is conducted by modulating the relative angular position of the circular histogram center of mass of one numerical attribute. We theoretically demonstrate the robustness performance of our scheme against most common attacks (i.e., tuple insertion and deletion). This makes it suitable for copyright protection, owner identification, or traitor tracing purposes. We further verify experimentally these theoretical limits within the framework of a medical database of more than one half million of inpatient hospital stay records. Under the assumption imposed by the central limit theorem, experimental results fit the theory. We also compare our approach with two efficient schemes so as to prove its benefits.