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

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.