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.

Different data mining algorithms and techniques are developed for finding meaningful and important patterns from big databases. Nowadays databases are often divided between multiple organizations or users for the reason of geographical remoteness, but the most important limit is protecting privacy. Every participant involved in data analysis wants to keep its own data private and secure because of law regulations, reasons of economy or securing know-how. Secure multiparty computations are designed for data mining in a multiparty environment, where it is very important to keeping the privacy of the input (and possibly output) data. Data clustering is widely used in many fields of human activity like informatics, network traffic analysis, economics, biology and medicine. C-means is the data clustering method by which analytics can divide data objects into several fuzzy clusters; one object may be include into more than one cluster. This article presents protocols for preserving privacy in the process of cluster analysis using C-means. The protocols allow the implementation of C-means algorithm for several parties both to horizontally partitioned data and vertically partitioned data.