Privacy preserving clustering over horizontal and vertical partitioned data
Mina Sheikhalishahi, Fabio Martinelli · 2017
This paper presents a framework for constructing a hierarchical categorical clustering algorithm on horizontal and vertical partitioned dataset. It is assumed that data is distributed between two parties, such that for general benefits both are willing to detect the clusters on whole dataset, but for privacy concerns, they refuse to share the original datasets. To this end, we propose algorithms based on secure weighted average protocol and secure number comparison protocol, to securely compute the desired criteria in constructing clusters' scheme.