Privacy preserving extraction of fuzzy rules from distributed data | IEEE Conference Publication | IEEE Xplore

Privacy preserving extraction of fuzzy rules from distributed data


Abstract:

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...Show More

Abstract:

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. In this paper, we propose a method to extract global fuzzy rules from distributed data 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. Each data set can be characterized by comparing the local fuzzy rules for each distributed data to the global ones for all data. We illustrate a result for experiments using Wine data from UCI Machine Learning Repository.
Date of Conference: 07-10 July 2013
Date Added to IEEE Xplore: 07 October 2013
ISBN Information:
Print ISSN: 1098-7584
Conference Location: Hyderabad, India

References

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