Secure Metric-Based Index for Similarity Cloud

D 2012

Secure Metric-Based Index for Similarity Cloud

KOZÁK, Štěpán, David NOVÁK and Pavel ZEZULA

Basic information

Original name

Secure Metric-Based Index for Similarity Cloud

Authors

KOZÁK, Štěpán (203 Czech Republic, belonging to the institution), David NOVÁK (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, guarantor, belonging to the institution)

Edition

7482. vyd. Berlin / Heidelberg, Secure Data Management : Proceedings of 9th VLDB Workshop, SDM 2012, Istanbul, Turkey, August 27, 2012, p. 130-147, 18 pp. 2012

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

DOI

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/12:00057633

Organization unit

Faculty of Informatics

ISBN

978-3-642-32872-5

ISSN

DOI

http://dx.doi.org/10.1007/978-3-642-32873-2_9

Keywords in English

similarity search; data privacy; cloud computing; data security

Abstract

V originále

We propose a similarity index that ensures data privacy and thus is suitable for search systems outsourced in a cloud. The proposed solution can exploit existing efficient metric indexes based on a fixed set of reference points. The method has been fully implemented as a security extension of an existing established approach called M-Index. This Encrypted M-Index supports evaluation of standard range and nearest neighbors queries both in precise and approximate manner. In the first part of this work, we analyze various levels of privacy in existing or future similarity search systems; the proposed solution tries to keep a reasonable privacy level while relocating only the necessary amount of work from server to an authorized client. The Encrypted M-Index has been tested on three real data sets with focus on various cost components.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

VF20102014004, research and development project

Name: Multimediální analýza (Acronym: Multimediální analýza)

Investor: Ministry of the Interior of the CR

Files attached

published.pdf

Find similar documents

Citovat

KOZÁK, Štěpán, David NOVÁK and Pavel ZEZULA. Secure Metric-Based Index for Similarity Cloud. In Jonker, Willem and Petkovic, Milan. Secure Data Management : Proceedings of 9th VLDB Workshop, SDM 2012, Istanbul, Turkey, August 27, 2012. 7482nd ed. Berlin / Heidelberg: Springer, 2012, p. 130-147. ISBN 978-3-642-32872-5. Available from: https://dx.doi.org/10.1007/978-3-642-32873-2_9.

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   address = {Berlin / Heidelberg},
   booktitle = {Secure Data Management : Proceedings of 9th VLDB Workshop, SDM 2012, Istanbul, Turkey, August 27, 2012},
   doi = {http://dx.doi.org/10.1007/978-3-642-32873-2_9},
   edition = {7482},
   editor = {Jonker, Willem and Petkovic, Milan},
   keywords = {similarity search; data privacy; cloud computing; data security},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Berlin / Heidelberg},
   isbn = {978-3-642-32872-5},
   pages = {130-147},
   publisher = {Springer},
   title = {Secure Metric-Based Index for Similarity Cloud},
   url = {http://dx.doi.org/10.1007/978-3-642-32873-2_9},
   year = {2012}
}

TY  - JOUR
ID  - 1067779
AU  - Kozák, Štěpán - Novák, David - Zezula, Pavel
PY  - 2012
TI  - Secure Metric-Based Index for Similarity Cloud
PB  - Springer
CY  - Berlin / Heidelberg
SN  - 9783642328725
KW  - similarity search
KW  - data privacy
KW  - cloud computing
KW  - data security
UR  - http://dx.doi.org/10.1007/978-3-642-32873-2_9
L2  - http://dx.doi.org/10.1007/978-3-642-32873-2_9
N2  - We propose a similarity index that ensures data privacy and thus is suitable for search systems outsourced in a cloud. The proposed solution can exploit existing efficient metric indexes based on a fixed set of reference points. The method has been fully implemented as a security extension of an existing established approach called M-Index. This Encrypted M-Index supports evaluation of standard range and nearest neighbors queries both in precise and approximate manner. In the first part of this work, we analyze various levels of privacy in existing or future similarity search systems; the proposed solution tries to keep a reasonable privacy level while relocating only the necessary amount of work from server to an authorized client. The Encrypted M-Index has been tested on three real data sets with focus on various cost components.
ER  -

KOZÁK, Štěpán, David NOVÁK and Pavel ZEZULA. Secure Metric-Based Index for Similarity Cloud. In Jonker, Willem and Petkovic, Milan. \textit{Secure Data Management : Proceedings of 9th VLDB Workshop, SDM 2012, Istanbul, Turkey, August 27, 2012}. 7482nd ed. Berlin / Heidelberg: Springer, 2012, p.~130-147. ISBN~978-3-642-32872-5. Available from: https://dx.doi.org/10.1007/978-3-642-32873-2\_{}9.

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