A Sigmoid based Learning in Heterogeneous Distortion for Data Privacy
K Sandhya Rani Kundra1, J. Hyma2, P.V.G.D Reddy3, K. Venkata Rao4
1K.K.Sandhya Rani, Asst., Department of Information Technology, Gayatri Vidhya Parishad College of Engineering(A), Andhra Pradesh, India.
2Dr. J. Hyma, Associate Professor, Department of Computer Science Engineering, ANITS, Andhra Pradesh, India.
3Prof P.V.G.D Reddy, Vice Chancellor, Sr. Professor of Computer Science & Systems Engineering Department, Andhra University, Andhra Pradesh, India.
4Prof.K.Venkata Rao, Academic Dean and Professor, Computer Science & Systems Engineering Department, Andhra University, Andhra Pradesh, India.
Manuscript received on 26 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 3066-3070 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24170981119/2019©BEIESP | DOI: 10.35940/ijitee.K2417.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Neural network-based learning models along with an access to huge data have made a remarkable outcome in recent years. These models are contributing a lot to improvise the working dimensions of various domains like Speech recognition, Image processing, Text analysis and many more. The well represented data is the main resource in the current research, but this data is often privacy sensitive and it definitely needs a proper attention failing which leads to serious privacy concerns. The proposed work demonstrates how learning models can be applied to analyze the data sensitivity and classify them to various privacy classes. Once the privacy class distribution is performed the model applies Inverse laplacian query model to check the data utility. The data should not get compromised on utility with the curse of privacy. With this intention the given experimental study succeeded in training the network to perform privacy analysis under a modest privacy budget, complexity training efficiency and data utility.
Keywords: Neural Networks, Differential Privacy, Query Model, Data utility.
Scope of the Article: Web-Based Learning: Innovation and Challenges