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UOT 004
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ITI əməkdaşlarının elmi işləri - məqalə |
Biblioqrafik təsvir | Alguliyev , R.M. Protecting children on the internet using deep generative adversarial networks / R.M. Alguliyev , F.D. Abdullayeva , S.S. Ojagverdiyeva // International Journal of Computational Systems Engineering. - 2020. - N: 2, vol.6.- P. 84-90. | Annotasiya | In this paper, to control children’s access to malicious information on the Internet, a
data sanitisation method based on Deep Generative Adversarial Networks is proposed. According
to the proposed approach, an autoencoder inside the generator block by adding some noise
implements the transformation of sensitive attributes considered dangerous for children, and the
logistic regression inside the discriminator block performs the classification of the transformed
data. To maintain the usefulness of the information during data transformation, the privacy and
utility rates of the sanitised data are measured in terms of expected risk, and the optimal
consensus between these two parameters is achieved by applying the minimax algorithm. In the
experiments, the classification algorithm has recognised the class of sensitive data with low
accuracy, and the class of non-sensitive data with high accuracy. | Elektron variant | Elektron variant |
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