Rights protection for categorical data

A novel method of rights protection for categorical data through watermarking is introduced in this paper. New watermark embedding channels are discovered and associated novel watermark encoding algorithms are proposed. While preserving data quality requirements, the introduced solution is designed to survive important attacks, such as subset selection and random alterations. Mark detection is fully "blind" in that it doesn\´t require the original data, an important characteristic, especially in the case of massive data. Various improvements and alternative encoding methods are proposed and validation experiments on real-life data are performed. Important theoretical bounds including mark vulnerability are analyzed. The method is proved (experimentally and by analysis) to be extremely resilient to both alteration and data loss attacks, for example, tolerating up to 80 percent data loss with a watermark alteration of only 25 percent.