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

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

Research output: Chapter in book/report/conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Pages1730-1733
Number of pages4
ISBN (Electronic)9781728129037
DOIs
Publication statusPublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: 20 Apr 202024 Apr 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
CityDallas
Period20/04/2024/04/20

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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