Institute of Mathematics and Informatics – Bulgarian Academy of Sciences
Citation:
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
Series/Report no.:
Science Series "Innovative STEM Education", volume 05;12
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.
