Authors:
Mona Hamidi
1
;
Mina Sheikhalishahi
2
and
Fabio Martinelli
2
Affiliations:
1
Universita di Siena, Italy
;
2
Consiglio Nazionale delle Ricerche, Italy
Keyword(s):
Privacy, Hierarchical Clustering, Data Sharing, Secure Two-party Computation, Distributed Clustering.
Related
Ontology
Subjects/Areas/Topics:
Information and Systems Security
;
Privacy Enhancing Technologies
Abstract:
This paper presents a framework for secure two-party agglomerative hierarchical clustering construction over
partitioned data. It is assumed that data is distributed between two parties horizontally, such that for mutual
benefits both parties are willing to identify clusters on their data as a whole, but for privacy restrictions, they
avoid to share their datasets. To this end, in this study, we propose general algorithms based on secure scalar
product and secure hamming distance computation to securely compute the desired criteria in constructing
clusters’ scheme. The proposed approach covers all possible secure agglomerative hierarchical clustering
construction when data is distributed between two parties, including both numerical and categorical data.