Protecting data privacy in growing neural gas

Growing neural gas is a well-known algorithm in evolutionary computing. It is very effective for training neural networks. However, if the training data for growing neural gas comes from two different parties, privacy concerns may become a hurdle for using this algorithm: Each party may not be willing to reveal her own data to the other, although she wants to collaborate with the other party in running the growing neural gas algorithm on their joint data. In this paper, we propose a privacy-preserving algorithm for growing neural gas with training data from two parties. Our algorithm allows two parties to jointly execute the growing neural gas algorithm without revealing any party’s data to the other. Our algorithm is secure in that it leaks no knowledge about any participant’s data to the other. Experiments on the real-world data show that our algorithm is very efficient.

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