Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation

Liu, Bochao; Lu, Jianghu; Wang, Pengju; Zhang, Junjie; Zeng, Dan; Qian, Zhenxing; Ge, Shiming

doi:10.1109/mmsp55362.2022.9950001

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the students are concerned about the privacy of their data and the way it is used. Therefore, they require high demands not only on a technologically oriented and high-quality learning process, but also that their data are protected and secure [5]. One contemporary data privacy technology, which is applied in this work to ensure the educational privacy of the students, is based on the generation of synthetic data.…”

Section: Introductionmentioning

confidence: 99%

Towards Independent Students’ Activities, Online Environment and Learning Performance: An Investigation through Synthetic Data and Artificial Neural Networks

Ivanova

Petrova

2023

Informatics

View full text Add to dashboard Cite

During the pandemic, universities were forced to convert their educational process online. Students had to adapt to new educational conditions and the proposed online environment. Now, we are back to the traditional blended learning environment and wish to understand the students’ attitudes and perceptions of online learning, knowing that they are able to compare blended and online modes. The aim of this paper is to present the performed predictive analysis regarding the students’ online learning performance taking into account their opinion. The predictive models are created through a supervised machine learning algorithm based on Artificial Neural Networks and are characterized with high accuracy. The analysis is based on generated synthetic datasets, ensuring a high level of students’ privacy preservation.

show abstract

Section: Introductionmentioning

confidence: 99%