Watermarking Numerical Data in the Presence of Noise

Data mining aims at extracting knowledge from data. Sometimes this requires the data owner to make data available to the data analyst. Unless the analyst is trusted by the data owner, the latter may wish to have his intellectual property rights protected. Watermarking is a way to provide some protection, not only for multimedia data, but also for numerical data. It consists of imperceptibly embedding a secret mark into the data to be protected. The mark can later be used to resolve any subsequent disputes on data ownership. This paper presents a watermarking method that embeds a watermark into each attribute of a multivariate continuous numerical dataset. This watermarking method is shown to be robust against random noise addition attacks. Data quality is assured to the extent that the watermarked data nearly preserve the attribute means and the covariance matrix from the original dataset. Our proposal is the first known watermarking system for multivariate numerical datasets with such robustness and quality properties.