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  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Evaluating The Utility Of Data Integration With Synthetic Data And Statistical Matching.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Evaluating The Utility Of Data Integration With Synthetic Data And Statistical Matching.

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Evaluating the utility of data integration with synthetic data and statistical matching.

Eunjeong Ji1, Jung Hun Ohn2, Hyemin Jo2,3

  • 1Division of Statistics, Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, 13620, South Korea.

Scientific Reports
|September 1, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Synthetic data integration can maintain data utility while reducing privacy risks. This study found that using synthetic data for statistical matching offers comparable accuracy to real data, especially when integrating datasets.

Keywords:
Biomedical dataStatistical matchingSynthetic data

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Area of Science:

  • Bioinformatics
  • Data Science
  • Epidemiology

Background:

  • Data integration improves dataset utility but poses privacy risks.
  • Synthetic data is a potential privacy-preserving solution.
  • The role of synthetic data in data integration requires further investigation.

Purpose of the Study:

  • To assess synthetic data integration's impact on data utility.
  • To evaluate varying common variables in statistical matching.
  • To explore synthetic-real dataset combinations in donor-recipient scenarios.

Main Methods:

  • Utilized the Korean Genome and Epidemiology Study (KoGES) cohort.
  • Generated multiple synthetic datasets with varied common variables.
  • Performed statistical matching using the nearest-neighbor hotdeck method.

Main Results:

  • All-available matched synthetic data generally outperformed other conditions.
  • Clinically relevant matching variables sometimes showed equivalent performance.
  • Synthetic data demonstrated comparable model accuracy to real data.

Conclusions:

  • Statistically matched synthetic data offers utility comparable to real data.
  • This approach can reduce privacy risks while preserving data utility.
  • Further research is needed to fully understand performance differences.