title: Privacy-preserving restricted boltzmann machine.
abstract: 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.
authors.affiliation: Computer Science and Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Li
authors.affiliation: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China ; Computer Science and Technology Department, Nanjing University, Nanjing 210046, China.
authors.forename: Yuan
authors.identifier: 0000-0001-9682-5231
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Tian Jia Bing Hall, Nanjing Normal University, Nanjing 210097, China.
authors.forename: Yue
authors.identifier: 0000-0003-2667-564X
authors.initials: Y
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D016494:Computer Security; D057225:Data Mining; D015233:Models, Statistical; D018907:Privacy; D015203:Reproducibility of Results; D012944:Social Support
references.citation: IEEE Trans Neural Netw. 2009 Oct;20(10):1554-64
references.pmid: 19709975
references.citation: Proc Natl Acad Sci U S A. 1982 Apr;79(8):2554-8
references.pmid: 6953413