Systematic Assessment of Tabular Data Synthesis Algorithms

• URL: http://arxiv.org/abs/2402.06806v2
• Date: Sat, 13 Apr 2024 03:11:56 GMT
• Title: Systematic Assessment of Tabular Data Synthesis Algorithms
• Authors: Yuntao Du, Ninghui Li,
• Abstract summary: We present a systematic evaluation framework for assessing data synthesis algorithms. We introduce a set of new metrics in terms of fidelity, privacy, and utility to address their limitations. Based on the proposed metrics, we also devise a unified objective for tuning, which can consistently improve the quality of synthetic data.
• Score: 9.08530697055844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data synthesis has been advocated as an important approach for utilizing data while protecting data privacy. A large number of tabular data synthesis algorithms (which we call synthesizers) have been proposed. Some synthesizers satisfy Differential Privacy, while others aim to provide privacy in a heuristic fashion. A comprehensive understanding of the strengths and weaknesses of these synthesizers remains elusive due to drawbacks in evaluation metrics and missing head-to-head comparisons of newly developed synthesizers that take advantage of diffusion models and large language models with state-of-the-art marginal-based synthesizers. In this paper, we present a systematic evaluation framework for assessing tabular data synthesis algorithms. Specifically, we examine and critique existing evaluation metrics, and introduce a set of new metrics in terms of fidelity, privacy, and utility to address their limitations. Based on the proposed metrics, we also devise a unified objective for tuning, which can consistently improve the quality of synthetic data for all methods. We conducted extensive evaluations of 8 different types of synthesizers on 12 real-world datasets and identified some interesting findings, which offer new directions for privacy-preserving data synthesis.

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