Secure Fog-Based E-Learning Scheme

Abstract

Nowadays, several universities and institutions make profit from the information technologies to enhance and develop their educational strategies and attract more learners. Therefore, distance learning (e-learning) and learning-on-the-go are technologies adopted by universities and service providers to afford more flexible education system. In fact, e-learning is gaining popularity worldwide and the number of learners enrolled in on-line courses is growing. This trend is explained mainly by the opportunities provided by Cloud Computing. In the cloud based educational context, the security factor in sharing the educational content is important and poses several security challenges, such as fine-grained access control and security preservation of content learning. Moreover, there is emergence of the new concept of User-Fog-Cloud architecture to bring closer the services to the client. In this paper, a new fog computing e-learning scheme is provided. Specifically, the proposed solution extends learning content from the cloud to the edge of the network. It can improve the efficiency of learning data analysis, reduces the encryption burden in terms of computation cost on user's devices by offloading part of encryption cost to fog servers and provides fine grained access control to learning content by encrypting the course and the exam with different cryptographic techniques like IBBE and CP-ABE. Further, we present a profile matching mechanism that helps teachers to find colleagues within their vicinity in an efficient and secure way. Security analysis shows that our scheme can achieve data confidentiality, fine-grained access control, collusion resistance and unforgeability. Performance evaluations demonstrate the efficiency of our solution, especially in terms of encryption computation costs.