Towards Principled Assessment of Tabular Data Synthesis Algorithms

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 lacking principled evaluation metrics and missing head-to-head comparisons of newly developed synthesizers that take advantage of diffusion models and …

aim algorithms assessment call cs.cr cs.db cs.lg data data privacy differential privacy fashion important large privacy protecting understanding