Privacy-Preserving Building of Self-Organizing Maps

Various data mining techniques are designed for extracting significant and valuable patterns from, huge databases. Today databases are often divided between several organizations for the reason of limitations like geographical remoteness, but the most important limit, is preserving privacy, unwillin...

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Published in:Journal of Siberian Federal University. Mathematics & Physics 2015-10, Vol.8 (4), p.478
Main Authors: Vashkevich, Alexey V, Zhukov, Vadim G, Semenkin, Eugene S
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container_title Journal of Siberian Federal University. Mathematics & Physics
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creator Vashkevich, Alexey V
Zhukov, Vadim G
Semenkin, Eugene S
description Various data mining techniques are designed for extracting significant and valuable patterns from, huge databases. Today databases are often divided between several organizations for the reason of limitations like geographical remoteness, but the most important limit, is preserving privacy, unwillingness of data disclosing. Every party involved in analysis wants to keep its own information private because of legal regulations and reasons of know-how. Secure multiparty computations are designed for data mining execution in a multiparty environment, where it is extremely important to maintain the privacy of the input (and possibly output) data. A self-organizing map is the data mining method by which analytics can display patterns on two-dimensional intuitive maps and recognize data clusters. This article presents protocols for preserving privacy in the process of building self-organizing maps. The protocols allow the implementation of a self-organizing map algorithm for two parties with horizontally partitioned data and for several parties with vertically partitioned data.
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title Privacy-Preserving Building of Self-Organizing Maps
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