Papers

Title: Security Enhancement for Multi-party ‘learning’ in Cloud Platform
Year of Publication: 2015
Publisher: International Journal of Computer Systems (IJCS)
ISSN: 2394-1065
Series: Volume 2, Number 10
Authors: SaiKrishna Reddy Palwai , Pranit Kumar Pandey, Sandeep CVS

Citation:

SaiKrishna Reddy Palwai , Pranit Kumar Pandey, Sandeep CVS, “Security Enhancement for Multi-party ‘learning’ in Cloud Platform ", International Journal of Computer Systems (IJCS), 2(10), pp: 427-430, October, 2015. BibTeX

@article{key:article,
	author = {SaiKrishna Reddy Palwai , Pranit Kumar Pandey, Sandeep CVS},
	title = {Security Enhancement for Multi-party ‘learning’ in Cloud Platform },
	journal = {International Journal of Computer Systems (IJCS)},
	year = {2015},
	volume = {2},
	number = {10},
	pages = {427-430},
	month = {October}
	}

Abstract

Cloud computing facilitates the owners to share data. Multiple parties join the learning process by conducting joint Back propagation neural network algorithm on the union of their respective data sets. During the learning process none of the party wants to disclose her/his private data to others. The limitations of the existing schemes are learning process for only two parties or the few ways in which the data is arbitrarily partitioned. The proposed solution allows two or more parties, each with an arbitrarily partitioned data set, to jointly conduct the learning. The solution is provided by the magnificent power of cloud computing. In the proposed scheme, each owner encrypts his/her private dataset locally through AES cryptography and uploads the cipher texts into the cloud. The cloud then executes most of the operations over cipher texts via BGN homomorphic algorithm. The cloud is unaware of the original dataset. The Back propagation learning takes place and the owners are benefited through collaborative learning. Thus the scalability of the learning process is improved and the privacy of the data is ensured.

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Keywords

Privacy preserving, learning, neural network, back-propagation, cloud computing, computation outsource, homomorphic encryption.