Encrypted data indexing for the secure outsourcing of spectral clustering

Liu, B; Chen, L; Zhu, X; Qiu, W

Encrypted data indexing for the secure outsourcing of spectral clustering

Liu, B Chen, L

Zhu, X Qiu, W

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Publication Type:: Journal Article
Citation:: Knowledge and Information Systems, 2019, 60 (3), pp. 1307 - 1328
Issue Date:: 2019-09-01

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Field	Value	Language
dc.contributor.author	Liu, B	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Qiu, W	en_US
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Knowledge and Information Systems, 2019, 60 (3), pp. 1307 - 1328	en_US
dc.identifier.issn	0219-1377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137648
dc.description.abstract	© 2018, Springer-Verlag London Ltd., part of Springer Nature. Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180100966
dc.relation.ispartof	Knowledge and Information Systems	en_US
dc.relation.isbasedon	10.1007/s10115-018-1262-2	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Information Systems	en_US
dc.title	Encrypted data indexing for the secure outsourcing of spectral clustering	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	60	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	60	en_US

Abstract:

© 2018, Springer-Verlag London Ltd., part of Springer Nature. Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.

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http://hdl.handle.net/10453/137648