Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FUZZ-IEEE.2013.6622440 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy set theory,privacy preserving extraction,data privacy,uci machine learning repository,learning (artificial intelligence),distributed data,privacy-preserving data mining,global fuzzy rule extraction,wine data,knowledge discovery,data mining,local fuzzy rules,distributed processing,distributed databases,servers,learning artificial intelligence,fuzzy sets | Data mining,Data stream mining,Neuro-fuzzy,Computer science,Fuzzy logic,Data pre-processing,Fuzzy set,Artificial intelligence,Information privacy,Private information retrieval,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7584 | 978-1-4799-0020-6 | 1 |
PageRank | References | Authors |
0.43 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiang Jinsai | 1 | 1 | 0.43 |
| Motohide Umano | 2 | 183 | 28.91 |
| Kazuhisa Seta | 3 | 1 | 0.43 |