Proving ownership over categorical data

This paper introduces a novel method of rights protection for categorical data through watermarking. We discover new watermark embedding channels for relational data with categorical types. We design novel watermark encoding algorithms and analyze important theoretical bounds including mark vulnerability. While fully preserving data quality requirements, our solution survives 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. We propose various improvements and alternative encoding methods. We perform validation experiments by watermarking the outsourced Wal-Mart sales data available at our institute. We prove (experimentally and by analysis) our solution to be extremely resilient to both alteration and data loss attacks, for example tolerating up to 80% data loss with a watermark alteration of only 25%.