An optimal secure and verifiable education content searching scheme for cloud-assisted edge computing

Abstract

Cloud-assisted edge computing transforms service delivery by providing cloud-like services near the radio access network, ensuring low-latency services for mobile devices and alleviating significant pressure on the backbone network. The encryption and storage of multimedia data on untrusted cloud servers have prompted the adoption of searchable encryption for controlled keyword searches on ciphertext. Elevating online learning experiences requires sophisticated data analysis techniques, with Big Data playing a substantial role in efficiently processing extensive learning data and enhancing the value of E-learning platforms. Over time, E-learning management systems evolve into rich repositories of learning materials, enabling subject matter experts to reuse content for creating new online materials. To tackle challenges, we propose an optimal, secure, and verifiable education content searching scheme tailored for cloud-assisted edge computing. We introduce a lightweight encryption scheme for secure data storage and design a multi-scale quantum harmonic oscillator model to enhance content searching effectiveness and ensure accurate retrieval of educational information. Performance evaluation, utilizing benchmark datasets like Kaggle web contented, the CISI test set, and data from MAHE University, consistently validates the efficiency and feasibility of the proposed scheme for E-learning applications.