KOZÁK, Štěpán. Efficiency and Security in Similarity Cloud Services. In Proceedings of the VLDB Endowment, Volume 6, Issue 12. New York: VLDB Endowment. p. 1450-1455. ISSN 2150-8097. 2013.
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Basic information
Original name Efficiency and Security in Similarity Cloud Services
Authors KOZÁK, Štěpán (203 Czech Republic, guarantor, belonging to the institution).
Edition New York, Proceedings of the VLDB Endowment, Volume 6, Issue 12, p. 1450-1455, 6 pp. 2013.
Publisher VLDB Endowment
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW publisher site
RIV identification code RIV/00216224:14330/13:00066556
Organization unit Faculty of Informatics
ISSN 2150-8097
Keywords (in Czech) podobnostní vyhledávání, služba, bezpečnost, outsourcing
Keywords in English outsourcing; similarity search; cloud; security; privacy
Tags DISA, metric indexing, privacy
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 30/4/2014 06:20.
Abstract
With growing popularity of cloud services, the trend in the industry is to outsource the data to a 3rd party system that provides searching in the data as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research of outsourcing classic exact-match or keyword search has been done. However, not much attention has been paid to the outsourcing of the similarity search, which becomes more and more important in information retrieval applications. In this work, we propose to the research community a model of outsourcing similarity search to the cloud environment (so called similarity cloud). We establish privacy and efficiency requirements to be laid down for the similarity cloud with an emphasis on practical use of the system in real applications; this requirement list can be used as a general guideline for practical system analysis and we use it to analyze current existing approaches. We propose two new similarity indexes that ensure data privacy and thus are suitable for search systems outsourced in a cloud. The balance of the first proposed technique EM-Index is more on the efficiency side while the other (DSH Index) shifts this balance more to the privacy side.
Links
GBP103/12/G084, research and development projectName: Centrum pro multi-modální interpretaci dat velkého rozsahu
Investor: Czech Science Foundation
MUNI/A/0739/2012, interní kód MUName: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)
Investor: Masaryk University, Category A
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