GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources

TL;DR

GenSyn is a multi-stage framework that synthesizes individual-level microdata by integrating macro data sources and auxiliary location information, improving data dependency preservation and privacy constraints.

Contribution

This paper introduces a novel multi-stage framework combining dependency graphs and Gaussian copulas to generate high-quality synthetic microdata from aggregated data sources.

Findings

Outperforms prior methods in dependency structure preservation

Effectively integrates macro and auxiliary data sources

Demonstrates robustness on real-world datasets

Abstract

Individual-level data (microdata) that characterizes a population, is essential for studying many real-world problems. However, acquiring such data is not straightforward due to cost and privacy constraints, and access is often limited to aggregated data (macro data) sources. In this study, we examine synthetic data generation as a tool to extrapolate difficult-to-obtain high-resolution data by combining information from multiple easier-to-obtain lower-resolution data sources. In particular, we introduce a framework that uses a combination of univariate and multivariate frequency tables from a given target geographical location in combination with frequency tables from other auxiliary locations to generate synthetic microdata for individuals in the target location. Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations. We perform extensive testing on two real-world datasets and demonstrate that our approach outperforms prior approaches in preserving the overall dependency structure of the data while also satisfying the constraints defined on the different variables.