E-Learning Privacy in the Context of Machine Learning

The present paper discusses the privacy issues associated with the usage of machine learning in e-learning systems. Although ML is a promising technology that increases personalization and engagement, it also poses significant security concerns in terms of gathering and using sensitive data. E-learning platforms are vulnerable to problems such as breaches, unauthorized access, and misutilization of personal information. The paper addresses two major concerns: recommendation engines and predictive performance systems, which emphasize the weaknesses and shortcomings in current data privacy and protection measures. In an attempt to address these challenges, the paper develops a privacy-preserving framework based on federated learning and data anonymization technologies. Together, these technologies work in the decentralization of data storage and reduction of exposure yet provide personalized experiences. There is a comparative study that will analyze whether such solutions could help improve data privacy and at the same time maintain functionality in ML. Finally, a balance between security and user experience is brought into discussion, but this time it puts emphasis on the need to have personalization without sacrificing privacy. The final result is a framework for privacy that supports the fulfillment of laws like GDPR, while in the meantime maintaining the best of the personalization that this brings with the ML approach so that an effective solution towards modern e-learning systems can be availed.