Paper Info
Title
Privacy preserving fuzzy co-clustering with distributed cooccurrence matrices
Abstract
Privacy preserving data mining is a promising topic for utilizing various personal information without fear of information leaks. Fuzzy co-clustering is a fundamental technique for summarizing mutual cooccurrence information among objects and items, and has been demonstrated to be useful in such applications as document analysis and collaborative filtering. In this paper, a secure framework for privacy preserving fuzzy co-clustering is proposed for handling both vertically and horizontally distributed cooccurrence matrices. Personal observation stored in each site is summarized into co-cluster structures with an encryption operation. The advantage of utilizing distributed cooccurrence matrices is demonstrated in several numerical experiments.
Year
DOI
Venue
2014
10.1109/SCIS-ISIS.2014.7044660
SCIS&ISIS
Keywords
Field
DocType
data mining,data privacy,matrix algebra,pattern clustering,collaborative filtering,distributed cooccurrence matrices,document analysis,encryption operation,personal information,privacy preserving data mining,privacy preserving fuzzy coclustering
Data mining,Document analysis,Collaborative filtering,Information retrieval,Matrix (mathematics),Computer science,Fuzzy logic,Encryption,Personally identifiable information,Biclustering,Privacy software
Conference
ISSN
Citations 
PageRank 
2377-6870
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Tanaka, D.131.59
Oda, T.221.23
K. Honda314512.73
Notsu, A.4134.23