Protecting children on the internet using deep generative adversarial networks Online publication date: Fri, 13-Nov-2020
by Rasim M. Alguliyev; Fargana J. Abdullayeva; Sabira S. Ojagverdiyeva
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 6, No. 2, 2020
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Systems Engineering (IJCSYSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook