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Automating Low-Fidelity Synthetic Data Generation from Metadata: A Reproducible Approach Using R

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The framework successfully generates low-fidelity synthetic data that closely mimics real datasets while ensuring privacy.
Synthetic data maintains the cardinality of categories within ±5% compared to real data, reducing identity exposure risk.
An automated pipeline utilizing metadata ensures structured synthetic data generation, streamlining researcher access.
Future refinements may enhance fidelity options and expand usability with the development of an R package.

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Automated pipeline produces synthetic data while preserving confidentiality in pseudonymised datasets, suggesting a scalable approach.

Linus Kipkorir Chirchir (2025) studied this question.

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