Kanwal Garg

Work place: Department of Computer Science and Application, Kurukshetra University, Kurukshetra, India

E-mail: gargkanwal@kuk.ac.in


Research Interests: Data Mining, Big Data


Kanwal Garg is an Assistant Professor at Department of Computer Science & Applications, Kurukshetra University, Kurukshetra. He holds an experience of 18 years. He received his Ph.D.  from GJU Science & Technology, Hisar. His area of research includes big data, Data Mining and Warehousing, Web Mining, Data Stream and OLAP cubes. He has published about 80 papers in National and International Journals.

Author Articles
A Novel Privacy Preservation Scheme by Matrix Factorized Deep Autoencoder

By Pooja Choudhary Kanwal Garg

DOI: https://doi.org/10.5815/ijcnis.2024.03.07, Pub. Date: 8 Jun. 2024

Data transport entails substantial security to avoid unauthorized snooping as data mining yields important and quite often sensitive information that must be and can be secured using one of the myriad Data Privacy Preservation methods. This study aspires to provide new knowledge to the study of protecting personal information. The key contributions of the work are an imputation method for filling in missing data before learning item profiles and the optimization of the Deep Auto-encoded NMF with a customizable learning rate. We used Bayesian inference to assess imputation for data with 13%, 26%, and 52% missing at random. By correcting any inherent biases, the results of decomposition problems may be enhanced. As the statistical analysis tool, MAPE is used. The proposed approach is evaluated on the Wiki dataset and the traffic dataset, against state-of-the-art techniques including BATF, BGCP, BCPF, and modified PARAFAC, all of which use a Bayesian Gaussian tensor factorization. Using this approach, the MAPE index is decreased for data which avails privacy safeguards than its corresponding original forms.

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