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Kadir Kesgin – Discover Education, 2025
The increasing demand for privacy-preserving, ethically aligned synthetic data generation in education has highlighted the limitations of existing tabular data generators. Traditional approaches often sacrifice fairness or privacy in pursuit of predictive accuracy, rendering them unsuitable for high-stakes academic settings. In this paper, we…
Descriptors: Synthesis, Data, Data Science, Data Use

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