# Adaptive and Robust Watermark for Generative Tabular Data

arXiv: https://arxiv.org/abs/2409.14700 · HTML: https://mlsanity.com/paper/2409.14700/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 19 of 19)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks](https://mlsanity.com/paper/1703.02930/) (2017)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [WAVES: Benchmarking the Robustness of Image Watermarks](https://mlsanity.com/paper/2401.08573/) (2024)
- [Watermark Stealing in Large Language Models](https://mlsanity.com/paper/2402.19361/) (2024)
- [Learning to Maximize Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2301.00557/) (2023)
- [A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning](https://mlsanity.com/paper/2311.05877/) (2023)
- [Synthetic Data Applications in Finance](https://mlsanity.com/paper/2401.00081/) (2023)
- [SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources](https://mlsanity.com/paper/2009.09471/) (2020)
- [A Comprehensive Survey of Watermarking Relational Databases Research](https://mlsanity.com/paper/1801.08271/) (2018)
- [Efficient Evaluation of the Probability Density Function of a Wrapped Normal Distribution](https://mlsanity.com/paper/1405.6397/) (2014)
- [Weighted Sampling Without Replacement from Data Streams](https://mlsanity.com/paper/1506.01747/) (2015)

## Built on by (most cited first, 3 of 3)

- [Watermarking Generative Categorical Data](https://mlsanity.com/paper/2411.10898/) (2024)
- [RaMark: Radioactive Watermarking for Generated Tabular Data](https://mlsanity.com/paper/2607.09000/) (2026)
- [Observation-Level Watermarking and Detection for Tabular Data](https://mlsanity.com/paper/2607.10554/) (2026)

## Abstract (arXiv)

In recent years, watermarking generative tabular data has become a prominent framework to protect against the misuse of synthetic data. However, while most prior work in watermarking methods for tabular data demonstrate a wide variety of desirable properties (e.g., high fidelity, detectability, robustness), the findings often emphasize empirical guarantees against common oblivious and adversarial attacks. In this paper, we study a flexible and robust watermarking algorithm for generative tabular data. Specifically, we demonstrate theoretical guarantees on the performance of the algorithm on metrics like fidelity, detectability, robustness, and hardness of decoding. The proof techniques introduced in this work may be of independent interest and may find applicability in other areas of machine learning. Finally, we validate our theoretical findings on synthetic and real-world tabular datasets.