Privacy-Preserving Synthetic Educational Data Generation

被引：2

作者：

Vie, Jill-Jenn ^{[1
]}

Rigaux, Tomas ^{[1
]}

Minn, Sein ^{[2
]}

机构：

[1] Inria Saclay, SODA, 1 Rue Honore dEstienne dOrves, F-91120 Palaiseau, France

[2] Inria Saclay, CEDAR, 1 Rue Honore dEstienne dOrves, F-91120 Palaiseau, France

来源：

EDUCATING FOR A NEW FUTURE: MAKING SENSE OF TECHNOLOGY-ENHANCED LEARNING ADOPTION, EC-TEL 2022 | 2022年 / 13450卷

关键词：

Generative models; Privacy; Item response theory;

D O I：

10.1007/978-3-031-16290-9_29

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：393 / 406

页数：14

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