Autor(s):

Malinka Ivanova, Technical University of Sofia, Faculty of Applied Mathematics and Informatics, Department of Informatics, Bulgaria, m_ivanova@tu-sofia.bg

Iskra Trifonova, Technical University of Sofia, Faculty of Applied Mathematics and Informatics, Department of Informatics, Bulgaria, izi_1976@abv.bg

https://doi.org/10.55630/STEM.2023.0512

Abstract:

    The paper summarizes contemporary methods and techniques for privacy preservation as some challenging issues are analyzed and presented. A bibliometric approach is utilized in order for the "big picture" to be outlined, showing current research status and trending topics. The bibliographic data are taken from scientific database Scopus and processed through specialized software. In addition, a detailed review is also performed to classify problems and solutions in the area of privacy preservation. Special attention is given to possibilities for data privacy protection in intelligent eLearning environments. The role of machine learning for creating more secure data models is pointed out. A conceptual model, summarizing the findings, is proposed.

Keywords:

Privacy Preservation; Machine Learning; eLearning Intelligent Environment;

Received:

24-04-2023

Accepted:

29-06-2023

Published:

24-07-2023

Cite (APA style):

Ivanova, M.; Trifonova, I. (2023). Privacy Preserving Techniques and Their Applications in Elearning, Science Series "Innovative STEM Education", volume 05, ISSN: 2683-1333, Institute of Mathematics and Informatics – Bulgarian Academy of Sciences, pp. 93-102, DOI: https://doi.org/10.55630/STEM.2023.0512

PDF file address:

http://www.math.bas.bg/vt/stemedu/books/05/STEM.2023.0512.pdf