Autor(s)

Malinka Ivanova, Iskra Trifonova, Galina Bogdanova

Published at

20th International Conference on Information Technology Based Higher Education and Training (ITHET), Antalya, Turkey, 2022, Institute of Electrical and Electronics Engineers (IEEE), 2023

Abstract

    Nowadays, a big amount of data is collected in eLearning environments, tracking students’ behavior and their performance of learning activities. Also, a part of educational data is used by third parties for statistical or research purposes. In many cases, the datasets are transferred and processed without any techniques for students’ identity protection and there are possibilities after attacks private and sensitive data to be revealed. The aim of the paper is to present the results from conducted explorations and analysis about applying privacy preserving algorithms k-anonymity and ( ε,δ )-differential privacy on data, collected in eLearning environment. The balance between students’ privacy protection and usefulness of output information is discussed considering several privacy parameters. Machine learning is used to predict the most suitable privacy models and in this way to support the decision making of data holder/owner.

Keywords

Data privacy; Privacy; Electronic learning; Machine learning algorithms; Machine learning; Predictive models; Prediction algorithms; privacy preservation; eLeanring; k-anonymity; (ε, δ)-differential privacy

Links

https://doi.org/10.1109/ITHET56107.2022.10031904

https://ieeexplore.ieee.org/document/10031904

Cite as

M. Ivanova, I. Trifonova and G. Bogdanova, (2023). Privacy Preservation in eLearning: Exploration and Analysis, 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET), Antalya, Turkey, 2022, pp. 1-8, doi: 10.1109/ITHET56107.2022.10031904.

The publication presents topics close to the project, but is not part of the project