Next Article in Journal
Relationship between Flowability, Entrapped Air Content and Strength of UHPC Mixtures Containing Different Dosage of Steel Fiber
Previous Article in Journal
Mutual Authentication Protocol for Role-Based Access Control Using Mobile RFID
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(8), 213; doi:10.3390/app6080213

SEPIM: Secure and Efficient Private Image Matching

1
Cluster and Grid Computing Lab, Services Computing Technology and System Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
Department of Computer Science, University of Basrah, Basrah 61001, Iraq
3
Department of Management, Southern Technical University, Basrah 61001, Iraq
This paper is an extended version of paper published in the 11th International Conference on Green, Pervasive and Cloud Computing (GPC’16), Xi’an, China, 6–8 May 2016.
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Fernández-Caballero
Received: 24 May 2016 / Revised: 7 July 2016 / Accepted: 22 July 2016 / Published: 29 July 2016
View Full-Text   |   Download PDF [5598 KB, uploaded 3 August 2016]   |  

Abstract

Matching a particular image within extensive datasets has become increasingly pressing in many practical fields. Hence, a number of matching methods have been developed when confidential images are used in image matching between a pair of security agencies, but they are limited by either search cost or search precision. In this paper, we propose a privacy-preserving private image matching scheme between two parties where images are confidential, namely secure and efficient private image matching (SEPIM). The descriptor set of the queried party needs to be generated and encrypted properly with the use of a secret key at the queried party side before being transferred to the other party. We present the development and validation of a secure scheme to measure the cosine similarity between two descriptor sets. To hasten the search process, we construct a tree-based index structure by utilizing the k-means clustering algorithm. The method can work without using any image encryption, sharing, and trusted third party. SEPIM is relatively efficient when set against other methods of searching images over plaintexts, and shows a higher search cost of just 14% and reduction in search precision of just 2%. We conducted several empirical analyses on real image collections to demonstrate the performance of our work. View Full-Text
Keywords: secure private image matching; feature protection; k-means clustering; secure multiparty computing (SMC); speeded up robust features (SURF) descriptors; homomorphic encryption secure private image matching; feature protection; k-means clustering; secure multiparty computing (SMC); speeded up robust features (SURF) descriptors; homomorphic encryption
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Abduljabbar, Z.A.; Jin, H.; Ibrahim, A.; Hussien, Z.A.; Hussain, M.A.; Abbdal, S.H.; Zou, D. SEPIM: Secure and Efficient Private Image Matching. Appl. Sci. 2016, 6, 213.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top