Privacy preserving extraction of fuzzy rules from distributed data with different attributes

Data mining has emerged as a significant technology for discovering knowledge in vast quantities of data. It is, however, accompanied by the danger that private information will be revealed in the processing of data mining. Hence, privacy-preserving data mining has received a growing amount of attention in recent years. We have proposed a method to extract global fuzzy rules from distributed data with the same attributes in a privacy-preserving manner. This method transfers only values necessary for the extraction process without collecting any data at one place and can obtain the global fuzzy rules at all places. In this paper, we propose a method to extract global fuzzy rules in a privacy-preserving manner from distributed data with different attributes based on the method for distributed data with the same attributes. We illustrate a result for experiments using Iris data by R.A. Fisher.