Privacy-Preserving Restricted Boltzmann Machine

Privacy-Preserving Restricted Boltzmann Machine

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a...

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Main Authors: Yu Li, Yuan Zhang, Yue Ji
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2014/138498
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spelling doaj-5391291becca40beb66b7110dd20c0232020-11-25T01:11:51ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182014-01-01201410.1155/2014/138498138498Privacy-Preserving Restricted Boltzmann MachineYu Li0Yuan Zhang1Yue Ji2Computer Science and Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260, USAState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, ChinaTian Jia Bing Hall, Nanjing Normal University, Nanjing 210097, ChinaWith the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model.http://dx.doi.org/10.1155/2014/138498
collection DOAJ
language English
format Article
sources DOAJ
author Yu Li
Yuan Zhang
Yue Ji
spellingShingle Yu Li
Yuan Zhang
Yue Ji
Privacy-Preserving Restricted Boltzmann Machine
Computational and Mathematical Methods in Medicine
author_facet Yu Li
Yuan Zhang
Yue Ji
author_sort Yu Li
title Privacy-Preserving Restricted Boltzmann Machine
title_short Privacy-Preserving Restricted Boltzmann Machine
title_full Privacy-Preserving Restricted Boltzmann Machine
title_fullStr Privacy-Preserving Restricted Boltzmann Machine
title_full_unstemmed Privacy-Preserving Restricted Boltzmann Machine
title_sort privacy-preserving restricted boltzmann machine
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2014-01-01
description With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model.
url http://dx.doi.org/10.1155/2014/138498
work_keys_str_mv AT yuli privacypreservingrestrictedboltzmannmachine
AT yuanzhang privacypreservingrestrictedboltzmannmachine
AT yueji privacypreservingrestrictedboltzmannmachine
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