A Privacy Protection Method for Learning Artificial Neural Network on Vertically Distributed Data

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’ data.

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