Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics
Qinyi Liu,
Mohammad Khalil,
Jelena Jovanovic
et al.
Abstract:Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonym ization and data misuse that current solutions struggle to address. Synthetic data emerges as a potential remedy, offering robust privacy protection. However, prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility. Synthetic data must not only enhance privacy but also remain practical for data analytic… Show more
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