Privacy-Preserving Synthetic Educational Data Generation

Jill-Jênn Vie, Tomas Rigaux, Sein Minn
7/7/2022
cs.CYcs.AIcs.CR

No official code found. Best implementation: Akulen/PrivGen (3 stars, MIT, last commit 2023-01-30).

Abstract

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.

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Code Implementations(1)

Akulen/PrivGenCommunity100%
MIT

RNN code used for "Privacy-Preserving Synthetic Educational Data Generation"

30Python, Jupyter NotebookJun 28, 20223 years agoMIT

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Cite this paper

@article{vie2022privacypreserving,
  title  = {Privacy-Preserving Synthetic Educational Data Generation},
  author = {Jill-Jênn Vie and Tomas Rigaux and Sein Minn},
  year   = {2022},
  eprint = {2207.03202},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.03202v1}
}

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