Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
This addresses privacy concerns for data sharing and analysis in sensitive domains, though it appears incremental as it builds on existing synthetic data generation methods.
The paper tackles the problem of generating synthetic tabular data that preserves privacy, introducing TabularARGN, which achieves competitive results in statistical similarity, machine learning utility, and detection robustness while maintaining computational efficiency.
Synthetic data generation has become essential for securely sharing and analyzing sensitive data sets. Traditional anonymization techniques, however, often fail to adequately preserve privacy. We introduce the Tabular Auto-Regressive Generative Network (TabularARGN), a neural network architecture specifically designed for generating high-quality synthetic tabular data. Using a discretization-based auto-regressive approach, TabularARGN achieves high data fidelity while remaining computationally efficient. We evaluate TabularARGN against existing synthetic data generation methods, showing competitive results in statistical similarity, machine learning utility, and detection robustness. We further perform an in-depth privacy evaluation using systematic membership-inference attacks, highlighting the robustness and effective privacy-utility balance of our approach.