Tamper detection and localization for categorical data using fragile watermarks

Today, database relations are widely used and distributed over the Internet. Since these data can be easily tampered with, it is critical to ensure the integrity of these data. In this paper, we propose to make use of fragile watermarks to detect and localize malicious alterations made to a database relation with categorical attributes. Unlike other watermarking schemes which inevitably introduce distortions to the cover data, the proposed scheme is distortion free. In our algorithm, all tuples in a database relation are first securely divided into groups according to some secure parameters. Watermarks are embedded and verified in each group independently. Thus, any modifications can be localized to some specific groups. Theoretical analysis shows that the probability of missing detection is very low.