Thursday 28th of March 2024
 

Preserving Privacy Using Gradient Descent Methods Applied for Neural Network with Distributed Datasets


Sachin P. Yadav and Amit B.Chougule

The learning problems have to be concerned about distributed input data, because of gradual expansion of distributed computing environment. It is important to address the privacy concern of each data holder by extending the privacy preservation concept to original learning algorithms, to enhance co-operations in learning. In this project, focus is on protecting the privacy in significant learning model i.e. Multilayer Back Propagation Neural Network using Gradient Descent Methods. For protecting the privacy of the data items (concentration is towards Vertically Partitioned Data and Horizontally Partitioned Data), semi honest model and underlying security of El Gamal Scheme is referred [7].

Keywords: Cryptography Techniques, Distributed Datasets, Gradient Descent Methods, Neural Network.

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ABOUT THE AUTHORS

Sachin P. Yadav
BE degree in Information Technology from Shivaji University Kolhpur,Maharashtra,India.Currently he is pursuing his ME in Computer Science and Engineering in D.Y.Patil College of Engineering and Technolgy,Kolhapur,Maharashtra and working as a Assistant Professor at Annasaheb Dange college of engineering,Ashta,Tal:Walwa,Dist Sangli.

Amit B.Chougule
M.Tech.Working as Professor at Bharati Vidyapeet’s College of Engineering, Kolhapur, Maharashtra, India.


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