Institutions collect massive learning traces but they may not disclose it for privacy issues. Synthetic data generation opens new opportunities for research in education. In this paper we present a generative model for educational data that can preserve the privacy of participants, and an evaluation framework for comparing synthetic data generators. We show how naive pseudonymization can lead to re-identification threats and suggest techniques to guarantee privacy. We evaluate our method on existing massive educational open datasets.
@article{arxiv.2207.03202,
title = {Privacy-Preserving Synthetic Educational Data Generation},
author = {Jill-Jênn Vie and Tomas Rigaux and Sein Minn},
journal= {arXiv preprint arXiv:2207.03202},
year = {2022}
}