Privacy-Preserving Attribute-Based Educational Service Recommendation in Online Education System

DOI

10.2991/978-94-6463-108-1_92How to use a DOI?

Keywords

ABKS; education services; anonymity; policy hiding; credibility evaluation

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.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lijuan Huan
AU  - Xueyan Liu
AU  - Ruirui Sun
AU  - Linpeng Li
PY  - 2022
DA  - 2022/12/30
TI  - Privacy-Preserving Attribute-Based Educational Service Recommendation in Online Education System
BT  - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
PB  - Atlantis Press
SP  - 826
EP  - 838
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-108-1_92
DO  - 10.2991/978-94-6463-108-1_92
ID  - Huan2022
ER  -