Protecting Student Data in ML Pipelines: An Overview of Privacy-Preserving ML

被引：1

作者：

Schleiss, Johannes ^{[1
]}

Guenther, Kolja ^{[1
]}

Stober, Sebastian ^{[1
]}

机构：

[1] Otto von Guericke Univ, Magdeburg, Germany

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II | 2022年 / 13356卷

关键词：

Student privacy; Safe learning analytics; Privacy protection; Data privacy; Privacy attacks;

D O I：

10.1007/978-3-031-11647-6_109

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The rise of Artificial Intelligence in Education opens up new possibilities for analysis of student data. However, the protection of private data in these applications is a major challenge. According to data regulations, the application designer is responsible for technical and organizational measures to ensure privacy. This paper aims to guide developers of educational platforms to make informed decisions about their use of privacy-preserving ML and, therefore, protect their student data.

引用

页码：532 / 536

页数：5

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