The article of the scientists of the Institute published in prestigious journal

An article “Privacy-preserving deep learning algorithm for big personal data analysis”  ” (DOI: https://doi.org/10.1016/j.jii.2019.07.002) co-authored by Vice-president of ANAS, director of the Institute of Information Technology, academician Rasim Alguliyev , head of the department, correspondent member of ANAS, doctor of technical sciences, Ramiz Aliguliyev and leading researcher, associate professor Farqana Abdullayeva was published in the  “Journal of Industrial Information Integration”,  published by “Elsevier”.

For privacy-preserving analyzing of big data, a deep learning method is proposed. The method transforms the sensitive part of the personal information into non-sensitive data. To implement this process, two-stage architecture is proposed. The modified sparse denoising autoencoder and CNN models have been used in the architecture. Modified sparse denoising autoencoder performs transformation of data and CNN classifies the transformed data. In order to achieve low loss in data transformation, the sparsification parameter is added to the objective function of the autoencoder by the Kullback–Leibler divergence function. The comparison of the proposed method with simple autoencoder is provided and experiments conducted on Cleveland medical dataset extracted from the Heart Disease dataset, Arrhythmia and Skoda datasets showed that the proposed method outperforms other conventional methods.

Note that the “Journal of Industrial Information Integration” is indexed and referenced in international scientific databases: Web of Science, Emerging Sources Citation Index (ESCI), Ei Compendex, EBSCOhost, Scopus, INSPEC.

The journal has the following scientific indicators:

CiteScore: 9.80 

Source Normalized Impact per Paper (SNIP): 3.336 

SCImago Journal Rank (SJR): 1.566

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