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2024 | OriginalPaper | Buchkapitel

Evaluation of Synthetic Data Generators on Complex Tabular Data

verfasst von : Oscar Thees, Jiří Novák, Matthias Templ

Erschienen in: Privacy in Statistical Databases

Verlag: Springer Nature Switzerland

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Abstract

Synthetic data generators are widely utilized to produce synthetic data, serving as a complement or replacement for real data. However, the utility of data is often limited by its complexity. The aim of this paper is to show their performance using a complex data set that includes cluster structures and complex relationships. We compare different synthesizers such as synthpop, Synthetic Data Vault, simPop, Mostly AI, Gretel, Realtabformer, and arf, taking into account their different methodologies with (mostly) default settings, on two properties: syntactical accuracy and statistical accuracy. As a complex and popular data set, we used the European Statistics on Income and Living Conditions data set. Almost all synthesizers resulted in low data utility and low syntactical accuracy.

The results indicated that for such complex data, simPop, a computational and methodological framework for simulating complex data based on conditional modeling, emerged as the most effective approach for static tabular data and is superior compared to other conditional or joint modelling approaches.

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Vorheriges Kapitel An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity

Nächstes Kapitel An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata

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Titel: Evaluation of Synthetic Data Generators on Complex Tabular Data
verfasst von: Oscar Thees
Jiří Novák
Matthias Templ
Verlag: Springer Nature Switzerland
Buch: Privacy in Statistical Databases
Print ISBN: 978-3-031-69650-3

Electronic ISBN: 978-3-031-69651-0

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-69651-0_13

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