JoWUA

Abstract

The growth in utilizing wireless mobile networks to provide e-learning platforms has raised concerns about security, privacy, and trust, particularly as the networks grow. This paper has suggested an innovative, secure, and scalable e-learning platform that is meant to overcome these issues. The framework proposed combines complex privacy-saving methods and a strong trust management system to protect the data of the users and provide trusted communication. The main contributions are a multi-layered security model based on encryption, data anonymization, and access control, as well as a trust-based reputation model through machine learning to determine the trustworthiness of users. In order to measure the performance and scalability of the framework, a large-scale experimental analysis of the proposed model was performed with an extensive comparison with already known solutions in the field of security, privacy, and scalability. The analysis was carried out statistically with the help of the measures of accuracy, precision, recall, and F1-score of the effectiveness of the framework. Based on the findings, the suggested system has a 30% better security, 25% higher privacy protection, and 20% better scalability than current strategies. Also, the framework has a trust accuracy of 92 %, which shows that the framework is effective in measuring the reliability of the user. The results indicate the possibility of the framework to offer a secure and scalable solution to e-learning applications in wireless mobile networks. To sum up, e-learning platforms have a great deal to gain in terms of security, privacy, and scalability with the proposed framework, which is a potential solution in the large-scale application of wireless mobile networks in the future.