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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 138498, 7 pages
Research Article

Privacy-Preserving Restricted Boltzmann Machine

Yu Li,1 Yuan Zhang,2,3 and Yue Ji4

1Computer Science and Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260, USA
2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China
3Computer Science and Technology Department, Nanjing University, Nanjing 210046, China
4Tian Jia Bing Hall, Nanjing Normal University, Nanjing 210097, China

Received 5 March 2014; Accepted 31 May 2014; Published 24 June 2014

Academic Editor: Tingting Chen

Copyright © 2014 Yu Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.