Title: Student privacy data encryption in network teaching platform based on dynamic key

Authors: Peng Qu; Dahui Li

Addresses: College of Life Sciences, Agriculture and Forestry, Qiqihar University, Qiqihar, Heilongjiang, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, China

Abstract: In order to improve the integrity and security of student privacy data on online teaching platforms, a dynamic key-based encryption method for student privacy data on online teaching platforms is proposed. Firstly, analyse the content of student privacy data on online teaching platforms, and use univariate feature extraction mining to mine student privacy data. Secondly, discrete wavelet transform is used to denoise the mined student privacy data to improve the quality of student privacy data. Finally, according to the dynamic key authentication process, complete the encryption of student privacy data. The experimental results show that in different privacy databases, the encryption time of our method is significantly shortened and the encryption integrity is effectively improved, with an average integrity of 96.23%. Therefore, it indicates that the practical application of this method has strong reliability and can improve the security of student privacy data.

Keywords: dynamic key; online teaching platform; student privacy data; data encryption.

DOI: 10.1504/IJCAT.2023.138836

International Journal of Computer Applications in Technology, 2023 Vol.73 No.4, pp.271 - 277

Received: 18 Sep 2023
Accepted: 15 Dec 2023
Published online: 31 May 2024 *

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