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2024 | OriginalPaper | Buchkapitel

A Case Study Exploring Data Synthesis Strategies on Tabular vs. Aggregated Data Sources for Official Statistics

verfasst von : Mohamed Aghaddar, Liu Nuo Su, Manel Slokom, Lucas Barnhoorn, Peter-Paul de Wolf

Erschienen in: Privacy in Statistical Databases

Verlag: Springer Nature Switzerland

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Abstract

In this paper, we investigate different approaches for generating synthetic microdata from open-source aggregated data. Specifically, we focus on macro-to-micro data synthesis. We explore the potential of the Gaussian copulas framework to estimate joint distributions from aggregated data. Our generated synthetic data is intended for educational and software testing use cases. We propose three scenarios to achieve realistic and high-quality synthetic microdata: (1) zero knowledge, (2) internal knowledge, and (3) external knowledge. The three scenarios involve different knowledge of the underlying properties of the real microdata, i.e., standard deviation, and covariate. Our evaluation includes matching tests to evaluate the privacy of the synthetic datasets. Our results indicate that macro-to-micro synthesis achieves better privacy preservation compared to other methods, demonstrating both the potential and challenges of synthetic data generation in maintaining data privacy while providing useful data for analysis.

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Titel: A Case Study Exploring Data Synthesis Strategies on Tabular vs. Aggregated Data Sources for Official Statistics
verfasst von: Mohamed Aghaddar
Liu Nuo Su
Manel Slokom
Lucas Barnhoorn
Peter-Paul de Wolf
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_28

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