Unlearnable examples (UE) have emerged as a practical mechanism to prevent unauthorized model training on private vision data, while extending this protection to tabular data is nontrivial. Tabular data in finance and healthcare is highly sensitive, yet existing UE methods transfer poorly because tabular features mix numerical and categorical constraints and exhibit saliency sparsity, with learning dominated by a few dimensions. Under a Spectral Dominance condition, we show certified unlearnability is feasible when the poison spectrum overwhelms the clean semantic spectrum. Guided by this, we propose Unlearnable Tabular Data via DecOuPled Shortcut EmbeddIng (UTOPIA), which exploits feature redundancy to decouple optimization into two channels: high saliency features for semantic obfuscation and low saliency redundant features for embedding a hyper correlated shortcut, yielding constraint-aware dominant shortcuts while preserving tabular validity. Extensive experiments across tabular datasets and models show UTOPIA drives unauthorized training toward near random performance, outperforming strong UE baselines and transferring well across architectures.

翻译：不可学习样本（UE）已成为防止未经授权模型在私有视觉数据上训练的有效机制，然而将此保护扩展到表格数据却非易事。金融和医疗领域的表格数据高度敏感，但现有UE方法迁移效果不佳，因为表格特征混合了数值与类别约束，并表现出显著性稀疏性，学习过程由少数维度主导。在谱主导条件下，我们证明当毒化谱压倒干净语义谱时，认证不可学习性是可行的。基于此，我们提出通过解耦捷径嵌入实现不可学习的表格数据（UTOPIA），该方法利用特征冗余将优化解耦为两个通道：高显著性特征用于语义混淆，低显著性冗余特征用于嵌入超相关捷径，从而在保持表格有效性的同时生成约束感知的主导捷径。在多种表格数据集和模型上的大量实验表明，UTOPIA能使未经授权的训练趋近随机性能，优于强UE基线，并能良好地跨架构迁移。