TabTokWak: Token(less)-Value Watermarking for Tabular Foundational Models

Shareable tables generated by Foundational Models (FM) are increasingly adopted in industry and scientific applications. With the advent of such data, governance of it becomes an ever more pressing matter. Towards this, watermarking is shown to be a promising approach. While existing watermarking approaches successfully embed watermarks in natural language, they fall short for tabular data due to the susceptibility of token-based representations to post-processing and the arbitrary column ordering. We propose a novel watermarking scheme for tabular data, Tabular Token(less) Watermark (TabTokWak), which embeds a watermarking signal on FM-generated tabular value and its tokenized representation. To be invariant to table generation order, our token watermark uses a seeded pattern to derive its watermark signal. In our experiments on five real-world datasets and a representative tabular FM, we demonstrate that TabTokWak enables effective data governance with high detection efficiency and minimal impact on data quality. Therewith, empirically demonstrating TabTokWak’s detectability and its robustness with a favorable quality trade-off. Code online: https://gitlab.ewi.tudelft.nl/dmls/publications/tabtokwak .