VCBIR: A verifiable search engine for content-based image retrieval

Shangwei Guo; Yang Ji; Ce Zhang; Cheng Xu; Jianliang Xu

doi:10.1109/ICDE48307.2020.00156

VCBIR: A verifiable search engine for content-based image retrieval

Shangwei Guo, Yang Ji, Ce Zhang, Cheng Xu, Jianliang Xu

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

We demonstrate vCBIR, a verifiable search engine for Content-Based Image Retrieval. vCBIR allows a small or medium-sized enterprise to outsource its image database to a cloud-based service provider and ensures the integrity of query processing. Like other common data-as-a-service (DaaS) systems, vCBIR consists of three parties: (i) the image owner who outsources its database, (ii) the service provider who executes the authenticated query processing, and (iii) the client who issues search queries. By employing a novel query authentication scheme proposed in our prior work [4], the system not only supports cloud-based image retrieval, but also generates a cryptographic proof for each query, by which the client could verify the integrity of query results. During the demonstration, we will showcase the usage of vCBIR and also provide attendees interactive experience of verifying query results against an untrustworthy service provider through graphical user interface (GUI).

Original language	English
Title of host publication	Proceedings of 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
Publisher	IEEE Computer Society
Pages	1730-1733
Number of pages	4
ISBN (Electronic)	9781728129037
ISBN (Print)	9781728129044
DOIs	https://doi.org/10.1109/ICDE48307.2020.00156
Publication status	Published - Apr 2020
Event	36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States Duration: 20 Apr 2020 → 24 Apr 2020 https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding (Link to conference proceedings)

Publication series

Name	Proceedings of International Conference on Data Engineering
ISSN (Print)	1084-4627
ISSN (Electronic)	2375-026X

Conference

Conference	36th IEEE International Conference on Data Engineering, ICDE 2020
Country/Territory	United States
City	Dallas
Period	20/04/20 → 24/04/20
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding (Link to conference proceedings)

Scopus Subject Areas

Software
Signal Processing
Information Systems

Access to Document

10.1109/ICDE48307.2020.00156

Cite this

@inproceedings{76a87c94850a4230b53dab3c61b47c7a,

title = "VCBIR: A verifiable search engine for content-based image retrieval",

abstract = "We demonstrate vCBIR, a verifiable search engine for Content-Based Image Retrieval. vCBIR allows a small or medium-sized enterprise to outsource its image database to a cloud-based service provider and ensures the integrity of query processing. Like other common data-as-a-service (DaaS) systems, vCBIR consists of three parties: (i) the image owner who outsources its database, (ii) the service provider who executes the authenticated query processing, and (iii) the client who issues search queries. By employing a novel query authentication scheme proposed in our prior work [4], the system not only supports cloud-based image retrieval, but also generates a cryptographic proof for each query, by which the client could verify the integrity of query results. During the demonstration, we will showcase the usage of vCBIR and also provide attendees interactive experience of verifying query results against an untrustworthy service provider through graphical user interface (GUI).",

author = "Shangwei Guo and Yang Ji and Ce Zhang and Cheng Xu and Jianliang Xu",

note = "Funding Information: This work is supported by HK-RGC under grants 12200819, 12201018, C6030-18GF, and C1008-16G. Publisher copyright: {\textcopyright} 2020 by The Institute of Electrical and Electronics Engineers, Inc.; 36th IEEE International Conference on Data Engineering, ICDE 2020 ; Conference date: 20-04-2020 Through 24-04-2020",

year = "2020",

month = apr,

doi = "10.1109/ICDE48307.2020.00156",

language = "English",

isbn = "9781728129044",

series = "Proceedings of International Conference on Data Engineering",

publisher = "IEEE Computer Society",

pages = "1730--1733",

booktitle = "Proceedings of 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding",

}

Guo, S, Ji, Y, Zhang, C, Xu, C & Xu, J 2020, VCBIR: A verifiable search engine for content-based image retrieval. in Proceedings of 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020. Proceedings of International Conference on Data Engineering, IEEE Computer Society, pp. 1730-1733, 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, United States, 20/04/20. https://doi.org/10.1109/ICDE48307.2020.00156

VCBIR: A verifiable search engine for content-based image retrieval. / Guo, Shangwei; Ji, Yang; Zhang, Ce et al.
Proceedings of 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020. IEEE Computer Society, 2020. p. 1730-1733 (Proceedings of International Conference on Data Engineering).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - VCBIR

T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020

AU - Guo, Shangwei

AU - Ji, Yang

AU - Zhang, Ce

AU - Xu, Cheng

AU - Xu, Jianliang

N1 - Funding Information: This work is supported by HK-RGC under grants 12200819, 12201018, C6030-18GF, and C1008-16G. Publisher copyright: © 2020 by The Institute of Electrical and Electronics Engineers, Inc.

PY - 2020/4

Y1 - 2020/4

N2 - We demonstrate vCBIR, a verifiable search engine for Content-Based Image Retrieval. vCBIR allows a small or medium-sized enterprise to outsource its image database to a cloud-based service provider and ensures the integrity of query processing. Like other common data-as-a-service (DaaS) systems, vCBIR consists of three parties: (i) the image owner who outsources its database, (ii) the service provider who executes the authenticated query processing, and (iii) the client who issues search queries. By employing a novel query authentication scheme proposed in our prior work [4], the system not only supports cloud-based image retrieval, but also generates a cryptographic proof for each query, by which the client could verify the integrity of query results. During the demonstration, we will showcase the usage of vCBIR and also provide attendees interactive experience of verifying query results against an untrustworthy service provider through graphical user interface (GUI).

AB - We demonstrate vCBIR, a verifiable search engine for Content-Based Image Retrieval. vCBIR allows a small or medium-sized enterprise to outsource its image database to a cloud-based service provider and ensures the integrity of query processing. Like other common data-as-a-service (DaaS) systems, vCBIR consists of three parties: (i) the image owner who outsources its database, (ii) the service provider who executes the authenticated query processing, and (iii) the client who issues search queries. By employing a novel query authentication scheme proposed in our prior work [4], the system not only supports cloud-based image retrieval, but also generates a cryptographic proof for each query, by which the client could verify the integrity of query results. During the demonstration, we will showcase the usage of vCBIR and also provide attendees interactive experience of verifying query results against an untrustworthy service provider through graphical user interface (GUI).

UR - http://www.scopus.com/inward/record.url?scp=85085860383&partnerID=8YFLogxK

U2 - 10.1109/ICDE48307.2020.00156

DO - 10.1109/ICDE48307.2020.00156

M3 - Conference proceeding

AN - SCOPUS:85085860383

SN - 9781728129044

T3 - Proceedings of International Conference on Data Engineering

SP - 1730

EP - 1733

BT - Proceedings of 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020

PB - IEEE Computer Society

Y2 - 20 April 2020 through 24 April 2020

ER -

VCBIR: A verifiable search engine for content-based image retrieval

Abstract

Publication series

Conference

Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this