Recommendation System for Privacy-Preserving Education Technologies
Authors: 
Shasha Xu, Xiufang Yin Academic Editor: Konstantinos DemertzisAuthors Info & Claims
Abstract
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals’ personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users’ privacy.
References
[1]
M. Al-Dojayli and A. Czekanski, “Integrated engineering design education: vertical and lateral learning,” Journal of Integrated Design and Process Science, vol. 21, no. 2, pp. 45–59, 2017.
[2]
L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, p. 53, 2021.
[3]
G. D’Acquisto, J. Domingo-Ferrer, P. Kikiras, V. Torra, Y.-A. de Montjoye, and A. Bourka, “Privacy by design in big data: an overview of privacy enhancing technologies in the era of big data analytics,” ArXiv151206000 Cs, 2015.
[4]
Z. Shen and T. Zhong, “Analysis of application examples of differential privacy in deep learning,” Computational Intelligence and Neuroscience, vol. 2021, 15 pages, 2021.
[5]
I. M. del Águila and J. del Sagrado, “Bayesian networks for enhancement of requirements engineering: a literature review,” Requirements Engineering, vol. 21, no. 4, pp. 461–480, 2016.
[6]
A. B. Mrad, V. Delcroix, S. Piechowiak, P. Leicester, and M. Abid, “An explication of uncertain evidence in Bayesian networks: likelihood evidence and probabilistic evidence,” Applied Intelligence, vol. 43, no. 4, pp. 802–824, 2015.
[7]
G. Canbek, S. Sagiroglu, T. T. Temizel, and N. Baykal, “Binary classification performance measures/metrics: a comprehensive visualized roadmap to gain new insights,” in Proceedings of the 2017 International Conference on Computer Science and Engineering (UBMK), pp. 821–826, Antalya, Turkey, October 2017.
[8]
O. O. Koyejo, N. Natarajan, P. K. Ravikumar, and I. S. Dhillon, “Consistent binary classification with generalized performance metrics,” p. 9, 2014, https://dblp.org/rec/conf/nips/KoyejoNRD14.html.
[9]
N. Elmrabit, F. Zhou, F. Li, and H. Zhou, “Evaluation of machine learning algorithms for anomaly detection,” in Proceedings of the 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp. 1–8, Dublin, Ireland, June 2020.
[10]
J. Bringer, H. Chabanne, and A. Patey, “Privacy-preserving biometric identification using secure multiparty computation: an overview and recent trends,” IEEE Signal Processing Magazine, vol. 30, no. 2, pp. 42–52, 2013.
[11]
C. C. Aggarwal, “Neighborhood-based collaborative filtering,” in Recommender Systems: The Textbook, C. C. Aggarwal, Ed., pp. 29–70, Springer International Publishing, Manhattan, NY, USA, 2016.
[12]
M. Deschênes, “Recommender systems to support learners' Agency in a Learning Context: a systematic review,” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, p. 50, 2020.
[13]
F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egyptian Informatics Journal, vol. 16, no. 3, pp. 261–273, 2015.
[14]
B. Alallawi, L. Denne, M. M. Apanasionok, C. F. Grindle, and R. P. Hastings, “Special educators' experiences of a numeracy intervention for autistic students,” European Journal of Special Needs Education, no. 0, pp. 1–14, 2021.
[15]
A. Demetriou, G. Spanoudis, and A. Mouyi, “Educating the developing mind: towards an overarching paradigm,” Educational Psychology Review, vol. 23, no. 4, pp. 601–663, 2011.
[16]
A. Klašnja-Milićević and M. Ivanović, “E-learning personalization systems and sustainable education,” Sustainability, vol. 13, no. 12, 12 pages, 2021.
[17]
H. J. Cha and M. L. Ahn, “Development of design guidelines for tools to promote differentiated instruction in classroom teaching,” Asia Pacific Education Review, vol. 15, no. 4, pp. 511–523, 2014.
[18]
C. Korkmaz and A.-P. Correia, “A review of research on machine learning in educational technology,” Educational Media International, vol. 56, no. 3, pp. 250–267, 2019.
[19]
J. T. Abbitt and W. J. Boone, “Gaining insight from survey data: an analysis of the community of inquiry survey using Rasch measurement techniques,” Journal of Computing in Higher Education, vol. 33, no. 2, pp. 367–397, 2021.
[20]
J. O. Berger, “Bayesian analysis,” in Statistical Decision Theory and Bayesian Analysis, J. O. Berger, Ed., pp. 118–307, Springer, New York, NY, USA, 1985.
[21]
M. S. Mahmud, J. Z. Huang, S. Salloum, T. Z. Emara, and K. Sadatdiynov, “A survey of data partitioning and sampling methods to support big data analysis,” Big Data Mining and Analytics, vol. 3, no. 2, pp. 85–101, 2020.
[22]
S. Blyumin, A. Pogodaev, and E. Khabibullina, “Graph-structural modeling of some special organizational systems,” in Proceedings of the 2020 2nd International Conference on Control Systems, Mathematical Modeling Automation and Energy Efficiency (SUMMA), pp. 279–283, Lipetsk, Russia, August 2020.
[23]
T. Alshalali, K. M'Bale, and D. Josyula, “Security and privacy of electronic health records sharing using hyperledger fabric,” in Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 760–763, Las Vegas, NV, USA, September 2018.
[24]
R. Hou, F. Tang, S. Liang, and G. Ling, “Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment,” Security and Communication Networks, vol. 2021, pp. 1–11, 2021.
[25]
Z. Li, W. Xu, H. Shi, Y. Zhang, and Y. Yan, “Security and privacy risk assessment of energy big data in cloud environment,” Computational Intelligence and Neuroscience, vol. 2021, pp. 1–11, 2021.
[26]
S. Behera and J. R. Prathuri, “Application of homomorphic encryption in machine learning,” in Proceedings of the 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS), pp. 1–2, Bangalore, India, August 2020.
[27]
X. Su, T. M. Khoshgoftaar, and R. Greiner, “Imputed neighborhood based collaborative filtering,” in Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 633–639, Sydney, NSW, Australia, December 2008.
[28]
B. Bordel, R. Alcarria, and T. Robles, “Lightweight encryption for short-range wireless biometric authentication systems in Industry 4.0,” Integrated Computer-Aided Engineering, vol. 29, no. 2, pp. 153–173, 2022.
[29]
M. Iezzi, “Practical privacy-preserving data science with homomorphic encryption: an overview,” in Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), pp. 3979–3988, Atlanta, GA, USA, September 2020.
[30]
Q. Fu, Y. Tian, and J. Sun, “Modeling and simulation of dynamic lane reversal using a cell transmission model,” Journal of Intelligent Transportation Systems, no. 0, pp. 1–13, 2021.
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Copyright © 2022 Shasha Xu and Xiufang Yin.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Limited
London, United Kingdom
Publication History
Published: 01 January 2022
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