Related papers: Watermarking Generative Tabular Data

Watermarking Generative Tabular Data

URL: http://arxiv.org/abs/2405.14018v1
Date: Wed, 22 May 2024 21:52:12 GMT
Title: Watermarking Generative Tabular Data
Authors: Hengzhi He, Peiyu Yu, Junpeng Ren, Ying Nian Wu, Guang Cheng,
Abstract summary: We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity. We also demonstrate appealing robustness against additive noise attack.
Score: 39.31042783480766
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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