@inproceedings{a6d15953d2a94fa58a5b31b61ee29ba5,
title = "A privacy protection method for learning artificial neural network on vertically distributed data",
abstract = "For mining privacy data that can not be seen directly, the privacy preserving data mining (PPDM) is needed. As far as we know, for neural network learning on vertically distributed databases, there is no good enough PPDM method. For solving it, a privacy preserving method for learning neural networks on vertically distributed data is proposed by this paper. This method designs protocols to exchange essential information for learning neural networks without opening private data. The learning results with this proposed method are the same as the results with the original BP algorithm without considering privacy preservation. And, in the learning process, every node cannot get the details of other nodes{\textquoteright} data.",
keywords = "Neural network, Privacy preserving, Secure multi-party computation, Vertically distributed databases",
author = "Guang Li and Xiaohong Su and Yadong Wang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; International Conference on Mechatronics and Intelligent Robotics, ICMIR 2018 ; Conference date: 19-05-2018 Through 20-05-2018",
year = "2019",
doi = "10.1007/978-3-030-00214-5\_142",
language = "英语",
isbn = "9783030002138",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "1159--1167",
editor = "John Wang and Kevin Deng and Srikanta Patnaik and Zhengtao Yu",
booktitle = "Recent Developments in Mechatronics and Intelligent Robotics - Proceedings of International Conference on Mechatronics and Intelligent Robotics ICMIR2018",
address = "德国",
}