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Synthetic data generation: A tertiary study
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DOI：10.1016/j.ipm.2026.104715.png)
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Abstract
En 中文
• Conducts a tertiary study consolidating surveys on synthetic data generation. • Analyses 17 quality-appraised SDG surveys published between 2015 and 2025. • Identifies domain, methodological, and transparency gaps across surveys. • We propose a taxonomy assessing data fidelity, utility, diversity, and privacy.
Keywords:
Synthetic data generation
Tertiary study
Survey of surveys
Machine learning
Data privacy
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IF:
6.9
Papers:
330
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0