A Cryptographic Ensemble for Secure Third Party Data Analysis: Collaborative Data Clustering Without Data Owner Participation



Almutairi, ST, Coenen, FP ORCID: 0000-0003-1026-6649 and Dures, K
(2019) A Cryptographic Ensemble for Secure Third Party Data Analysis: Collaborative Data Clustering Without Data Owner Participation. Knowledge and Data Engineering.

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Abstract

This paper introduces the twin concepts Cryptographic Ensembles and Global Encrypted Distance Matrices (GEDMs), designed to provide a solution to outsourced secure collaborative data clustering. The cryptographic ensemble comprises: Homomorphic Encryption (HE) to preserve raw data privacy, while supporting data analytics; and Multi-User Order Preserving Encryption (MUOPE) to preserve the privacy of the GEDM. Clustering can therefore be conducted over encrypted datasets without requiring decryption or the involvement of data owners once encryption has taken place, all with no loss of accuracy. The GEDM concept is applicable to large scale collaborative data mining applications that feature horizontal data partitioning. In the paper DBSCAN clustering is adopted for illustrative and evaluation purposes. The results demonstrate that the proposed solution is both efficient and accurate while maintaining data privacy.

Item Type: Article
Uncontrolled Keywords: Data mining as a service, Privacy preserving data mining, Security, Data outsourcing
Depositing User: Symplectic Admin
Date Deposited: 16 Aug 2019 13:22
Last Modified: 13 May 2021 07:11
DOI: 10.1016/j.datak.2019.101734
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3051953