Outsourced similarity search on metric data assets

Handle URI:
http://hdl.handle.net/10754/562069
Title:
Outsourced similarity search on metric data assets
Authors:
Yiu, Man Lung; Assent, Ira; Jensen, Christian Søndergaard; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Institute of Electrical and Electronics Engineers
Journal:
IEEE Transactions on Knowledge and Data Engineering
Issue Date:
Feb-2012
DOI:
10.1109/TKDE.2010.222
Type:
Article
ISSN:
10414347
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYiu, Man Lungen
dc.contributor.authorAssent, Iraen
dc.contributor.authorJensen, Christian Søndergaarden
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2015-08-03T09:44:01Zen
dc.date.available2015-08-03T09:44:01Zen
dc.date.issued2012-02en
dc.identifier.issn10414347en
dc.identifier.doi10.1109/TKDE.2010.222en
dc.identifier.urihttp://hdl.handle.net/10754/562069en
dc.description.abstractThis paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries.en
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.subjectand protectionen
dc.subjectintegrityen
dc.subjectQuery processingen
dc.subjectSecurityen
dc.titleOutsourced similarity search on metric data assetsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineeringen
dc.contributor.institutionDepartment of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kongen
dc.contributor.institutionDepartment of Computer Science, Aarhus University, Aarhus DK-8200, Denmarken
kaust.authorKalnis, Panosen
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