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Authors: Nawal Almutairi 1 ; Frans Coenen 2 and Keith Dures 2

Affiliations: 1 Department of Computer Science, University of Liverpool, U.K., Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh and Saudi Arabia ; 2 Department of Computer Science, University of Liverpool and U.K.

ISBN: 978-989-758-330-8

Keyword(s): Privacy Preserving Data Mining, Secure Clustering, Homomorphic Encryption, Order Preserving Encryption, Secure Chain Distance Matrices.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Secure data mining has emerged as an essential requirement for exchanging confidential data in terms of third party (outsourced) data analytics. An emerging form of encryption, Homomorphic Encryption, allows a limited amount of data manipulation and, when coupled with additional information, can facilitate secure third party data analytics. However, the resource required is substantial which leads to scalability issues. Moreover, in many cases, data owner participation can still be significant, thus not providing a full realisation of the vision of third party data analytics. The focus of this paper is therefore scalable and secure third party data clustering with only very limited data owner participation. To this end, the concept of Secure Chain Distance Matrices is proposed. The mechanism is fully described and analysed in the context of three different clustering algorithms. Excellent evaluation results were obtained.

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Paper citation in several formats:
Almutairi, N.; Coenen, F. and Dures, K. (2018). Data Clustering using Homomorphic Encryption and Secure Chain Distance Matrices.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 41-50. DOI: 10.5220/0006890800410050

@conference{kdir18,
author={Nawal Almutairi. and Frans Coenen. and Keith Dures.},
title={Data Clustering using Homomorphic Encryption and Secure Chain Distance Matrices},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={41-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006890800410050},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Data Clustering using Homomorphic Encryption and Secure Chain Distance Matrices
SN - 978-989-758-330-8
AU - Almutairi, N.
AU - Coenen, F.
AU - Dures, K.
PY - 2018
SP - 41
EP - 50
DO - 10.5220/0006890800410050

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