Observation-Level Watermarking and Detection for Tabular Data

Statistical Seminar

Organizer：

吴宇楠

Speaker：

Xuan Bi, the Carlson School of Management, University of Minnesota

Time：

Tuesday, 14:00-15:00, June 30, 2026

Venue：

C548, Shuangqing Complex Building A

Title：

Observation-Level Watermarking and Detection for Tabular Data

Abstract:

With the development of generative Al, watermark techniques have been widely used to detect the authenticity of Al-generated data and protect the rights of users and creators. While it is already well applied in data types including imaging and text data, watermarking tabular data are still under-explored. Existing methods primarily focus on numerical data, leaving discrete, categorical, and mixed data less studied. In this work, we propose STAMP (Single-observation Tabular Attribution and Marking Procedure), a novel framework for watermarking tabular data that can accommodate and preserve a wide range of distributions. We also develop a corresponding detection mechanism, which can reliably identify watermarks even when the sample size is as small as one. We establish theoretical guarantees for asymptotic consistency and detection accuracy. Finally, through extensive simulation studies and two real-data applications, we demonstrate that the proposed method is effective and robust to subsetting, while maintaining data fidelity and a high detection rate.