Privacy-Preserving Attribute-Based Educational Service Recommendation in Online Education System
Year: 2022
Type: conference-paper
Abstract: Educational service recommendation has attracted considerable attention since it can solve complicated educational tasks by gathering the wisdom of a crowd of teachers in recent years.In the education service recommendation system, parent (student) can send requirements to the education platform, and get a suitable teacher recommended.In the existing education service recommendation schemes, although parent (student) can send the basic requirements to get education service recommendations, they cannot set personalized requirements to obtain personalized education services.In addition, teacher' ability or credibility has not been concerned, and the privacy-preserving of tasks and task recipients have also been ignored.To address the above problems, this article proposes a privacy-preserving attribute-based education service recommendation scheme, which realizes fine-grained access control and keywords search for education services by using attribute-based searchable encryption (ABKS).Then, the anonymous key generation method is adopted, in which the attribute authority and the teacher interact to generate the key to ensure the security of the teacher's key.Besides, education platform can choose the best teacher to accept the task by evaluation mechanism.The security proof and performance analysis show that the scheme has strong security and practicality in the online education system.
Authors: Lijuan Huan, Xueyan Liu, Ruirui Sun, Linpeng Li
Institution: Northwest Normal University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4318474301