Title :
Privacy preserving extraction of fuzzy rules from distributed data
Author :
Jiang Jinsai ; Umano, Motohide ; Seta, Kazuhisa
Author_Institution :
Dept. of Math., Osaka Prefecture Univ., Sakai, Japan
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.
Keywords :
data mining; data privacy; distributed processing; fuzzy set theory; learning (artificial intelligence); UCI machine learning repository; Wine data; distributed data; global fuzzy rule extraction; knowledge discovery; local fuzzy rules; privacy preserving extraction; privacy-preserving data mining; Data privacy; Distributed databases; Educational institutions; Fuzzy sets; Servers;
Conference_Titel :
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on
Conference_Location :
Hyderabad
Print_ISBN :
978-1-4799-0020-6
DOI :
10.1109/FUZZ-IEEE.2013.6622440