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EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing
Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, College of Computer and Software, Nanjing University of Information Science & Technology.
Department of Business and Computer Science, Southwestern Oklahoma State University.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, College of Computer and Software, Nanjing University of Information Science & Technology.
Number of Authors: 4
2017 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 387, 195-204 p.Article in journal (Refereed) Published
Abstract [en]

The content-based image retrieval (CBIR) has been widely studied along with the increasing importance of images in our daily life. Compared with the text documents, images consume much more storage and thus are very suitable to be stored on the cloud servers. The outsourcing of CBIR to the cloud servers can be a very typical service in cloud computing. For the privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before being outsourced, which will cause the CBIR technologies in plaintext domain unusable. In this paper, we propose a scheme that supports CBIR over the encrypted images without revealing the sensitive information to the cloud server. Firstly, the feature vectors are extracted to represent the corresponding images. Then, the pre-filter tables are constructed with the locality-sensitive hashing to increase the search efficiency. Next, the feature vectors are protected by the secure k-nearest neighbor (kNN) algorithm. The security analysis and experiments show the security and efficiency of the proposed scheme.

Place, publisher, year, edition, pages
2017. Vol. 387, 195-204 p.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-61255DOI: 10.1016/j.ins.2016.12.030ISI: 000393267400014Scopus ID: 2-s2.0-85009223485OAI: oai:DiVA.org:ltu-61255DiVA: diva2:1059959
Note

Validerad; 2017; Nivå 2; 2017-01-30 (andbra)

Available from: 2016-12-27 Created: 2016-12-27 Last updated: 2017-03-14Bibliographically approved

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CiteExportLink to record
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