Geodesic
Privacy-preserving building of self-organizing maps
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 8 (2015) no. 4, pp. 478-486.

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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.
Keywords: secure multiparty computations, secure dot product, cluster analysis, self-organizing map. 
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Alexey V. Vashkevich; Vadim G. Zhukov; Eugene S. Semenkin. Privacy-preserving building of self-organizing maps. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 8 (2015) no. 4, pp. 478-486. http://geodesic.mathdoc.fr/item/JSFU_2015_8_4_a10/

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