Watermarking Generative Tabular Data
<|reference_start|>Watermarking Generative Tabular Data: In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity, and also demonstrates appealing robustness against additive noise attack. The general idea is to achieve the watermarking through a strategic embedding based on simple data binning. Specifically, it divides the feature's value range into finely segmented intervals and embeds watermarks into selected ``green list" intervals. To detect the watermarks, we develop a principled statistical hypothesis-testing framework with minimal assumptions: it remains valid as long as the underlying data distribution has a continuous density function. The watermarking efficacy is demonstrated through rigorous theoretical analysis and empirical validation, highlighting its utility in enhancing the security of synthetic and real-world datasets.<|reference_end|>
@article{he2024watermarking,
    title={Watermarking Generative Tabular Data},
    author={Hengzhi He and Peiyu Yu and Junpeng Ren and Ying Nian Wu and Guang
  Cheng},
    journal={arXiv preprint arXiv:2405.14018},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14018},
    primaryClass={cs.CR cs.LG stat.AP}
}