TabularMark：为机器学习的表格数据集添加水印
TabularMark: Watermarking Tabular Datasets for Machine Learning
AI总结：
针对现有表格数据集水印方法属性不足、忽略下游ML模型效用的问题，提出基于假设检验的TabularMark方案，通过数据噪声划分嵌入水印、自定义阈值单比例z检验检测，在多属性适配和三大核心属性上表现更优。
摘要：
水印技术被广泛用于在保护共享数据所有权的同时保留数据效用。然而，现有的表格数据集水印方法在可检测性、非侵入性和鲁棒性这些理想属性上存在不足，且仅从数据统计的角度保留数据效用，忽略了在该数据集上训练的下游机器学习模型的性能。我们能否在不对表格数据集训练机器学习模型的效用造成显著损害的前提下，为其添加水印，同时阻止攻击者在被攻击的数据集上训练出可用的机器学习模型？本文提出了一种基于假设检验的水印方案TabularMark。嵌入过程中采用数据噪声划分进行数据扰动，该方法可适配数值型和类别型属性，同时保留数据效用。检测阶段采用自定义阈值的单比例z检验，能够可靠地判定水印是否存在。在真实数据集和合成数据集上的实验表明，TabularMark在可检测性、非侵入性和鲁棒性方面具有优势。
英文摘要：
Watermarking is broadly utilized to protect ownership of shared data while preserving data utility. However, existing watermarking methods for tabular datasets fall short on the desired properties (detectability, non-intrusiveness, and robustness) and only preserve data utility from the perspective of data statistics, ignoring the performance of downstream ML models trained on the datasets. Can we watermark tabular datasets without significantly compromising their utility for training ML models while preventing attackers from training usable ML models on attacked datasets? In this paper, we propose a hypothesis testing-based watermarking scheme, TabularMark. Data noise partitioning is utilized for data perturbation during embedding, which is adaptable for numerical and categorical attributes while preserving the data utility. For detection, a custom-threshold one proportion z-test is employed, which can reliably determine the presence of the watermark. Experiments on real-world and synthetic datasets demonstrate the superiority of TabularMark in detectability, non-intrusiveness, and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
