Comprehensive Evaluation of Tabular Data Synthesis Algorithms: Balancing Fidelity, Privacy, and Utility

This paper presents a systematic evaluation framework for assessing the performance of tabular data synthesis algorithms across three key dimensions: fidelity, privacy, and utility. The authors propose new metrics to address the limitations of existing evaluation approaches and conduct extensive experiments on a wide range of state-of-the-art synthesis algorithms.