























Abstract: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.
From: Dung Daniel Ngo [view email]
[v1]
Mon, 23 Sep 2024 04:37:30 UTC (213 KB)
[v2]
Fri, 6 Jun 2025 17:38:03 UTC (266 KB)
[v3]
Thu, 13 Nov 2025 19:56:13 UTC (153 KB)
[v4]
Wed, 8 Jul 2026 18:36:25 UTC (553 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。