Linear Fuzzy Clustering of Distributed Databases Considering Privacy Preservation

Abstract

Collaborative data clustering is a promising approach for extracting intrinsic cluster structures from distributed databases keeping personal privacy, where the goal of collaborative analysis is to find richer information rather than independent analysis of each database. In this research, a novel privacy preserving linear fuzzy clustering model is proposed by enhancing the scheme of k-Means-type model into Fuzzy c-Lines (FCL) in conjunction with cryptographic calculation. The element-wise clustering criterion enables to derive local principal component vectors in each data sources without handling fuzzy scatter matrices. The characteristics of the proposed method are demonstrated in an experiment with an artificial data set followed by an application to analysis of human behavior from sensor data.