Privacy Preserving Back-Propagation Learning Made Practical with Cloud Computing

Jiawei Yuan; Shucheng Yu

Security and Privacy in Communication Networks. 8th International ICST Conference, SecureComm 2012, Padua, Italy, September 3-5, 2012. Revised Selected Papers

Research Article

Privacy Preserving Back-Propagation Learning Made Practical with Cloud Computing

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@INPROCEEDINGS{10.1007/978-3-642-36883-7_18,
    author={Jiawei Yuan and Shucheng Yu},
    title={Privacy Preserving Back-Propagation Learning Made Practical with Cloud Computing},
    proceedings={Security and Privacy in Communication Networks. 8th International ICST Conference, SecureComm 2012, Padua, Italy, September 3-5, 2012. Revised Selected Papers},
    proceedings_a={SECURECOMM},
    year={2013},
    month={2},
    keywords={privacy reserving learning neural network back-propagation cloud computing computation outsource},
    doi={10.1007/978-3-642-36883-7_18}
}

Jiawei Yuan
Shucheng Yu
Year: 2013
Privacy Preserving Back-Propagation Learning Made Practical with Cloud Computing
SECURECOMM
Springer
DOI: 10.1007/978-3-642-36883-7_18

Jiawei Yuan¹^,*, Shucheng Yu¹^,*

1: University of Arkansas at Little Rock

*Contact email: jxyuan@ualr.edu, sxyu1@ualr.edu

Abstract

Back-propagation is an effective method for neural network learning. To improve the accuracy of the learning result, in practice multiple parties may want to collaborate by jointly executing the back-propagation algorithm on the union of their respective data sets. During this process no party wants to disclose her/his private data to others for privacy concerns. Existing schemes supporting this kind of collaborative learning just partially solve the problem by limiting the way of data partition or considering only two parties. There still lacks a solution for more general and practical settings wherein two or more parties, each with an arbitrarily partitioned data set, collaboratively conduct learning.

Keywords: privacy reserving learning neural network back-propagation cloud computing computation outsource

Published: 2013-02-15

: http://dx.doi.org/10.1007/978-3-642-36883-7_18