Efficient privacy-preserving anonymous authentication scheme for human predictive online education system

In recent years, online education systems (OES) are improved tremendously with the development of information communication technology. Also, OES provides the opportunity for the learners to predict and access the learning resources using Internet-of-Things (IoT) devices and it provides learning flexibility through the various artificial intelligence and soft computing approaches. The physical verification of the traditional education system is replaced with a secure authentication process for a human-centered predictive intelligence system. Many authentication schemes are available to provide authentication in human predictive OES, but they are suffering from authentication delay, computation complexity, communication cost, and user privacy. Hence, it is very difficult to provide data security using resource-limited IoT devices. In this work, a secure and efficient anonymous authentication scheme is introduced to avoid the mischievous learners and subject experts entering into OES. Also, the proposed scheme provides the essential security requirement of user privacy to OES users until they behave properly. If any chance for dispute, the system discloses the privacy of misbehaving users. The security and performance analysis section ensures that the proposed system provides a secure infrastructure to support sustainable education using resource-limited IoT devices by consuming very little computation and communication delay compared with other existing schemes.