Data Clustering using Homomorphic Encryption and Secure Chain Distance Matrices

Nawal Almutairi, Frans Coenen, Keith Dures

2018

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.

Paper Citation

in Harvard Style

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 (IC3K 2018) - Volume 1: KDIR; ISBN 978-989-758-330-8, SciTePress, pages 41-50. DOI: 10.5220/0006890800410050

in Bibtex Style

@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 (IC3K 2018) - Volume 1: KDIR},
year={2018},
pages={41-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006890800410050},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - 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
PB - SciTePress