Multi-Feature Membership Analysis for Tabular Regression Models: Towards Data Sovereignty

The intersection of machine learning (ML) and data privacy has become increasingly critical in safeguarding sensitive information. While membership inference attacks (MIAs) are often viewed as privacy threats, this work utilizes them as a tool for enforcing data sovereignty by detecting unauthorized data usage in ML models. While MIAs are well-studied for classification tasks, their application to regression models, particularly those handling tabular data, remains underexplored. This domain poses unique challenges due to its continuous outputs, high dimensionality, and diverse feature distributions. We present Multi-Feature Membership Analysis (MFMA), a framework that enhances membership inference in regression models through multiple complementary attack features. Our framework includes augmentation statistics, ensemble variation analysis, and targeted perturbation techniques, each capturing different behavioral signals of the model under black-box or semi-black-box access. Our comprehensive evaluation spans multiple datasets, model types, and attack scenarios. MFMA consistently outperforms baseline error-based attacks, with particularly notable improvements in the TPR at low FPR regime - a critical metric for real-world auditing and data ownership claims. These results demonstrate the feasibility and practical relevance of MIAs in tabular regression, and position MFMA as a step toward reliable data-use auditing in deployed ML systems, enabling organizations to enforce data sovereignty and enhance data privacy protection.