Table of Content

Open Access

ARTICLE

An Encrypted Image Retrieval Method Based on SimHash in Cloud Computing

Jiaohua Qin1, Yusi Cao1, Xuyu Xiang1, *, Yun Tan1, Lingyun Xiang2, Jianjun Zhang3
1 College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410004, China.
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
3 College of Engineering and Design, Hunan Normal University, Changsha, 410012, China.
* Corresponding Author: Xuyu Xiang. Email: .

Computers, Materials & Continua 2020, 63(1), 389-399. https://doi.org/10.32604/cmc.2020.07819

Received 02 July 2019; Accepted 11 July 2019; Issue published 30 March 2020

Abstract

With the massive growth of images data and the rise of cloud computing that can provide cheap storage space and convenient access, more and more users store data in cloud server. However, how to quickly query the expected data with privacy-preserving is still a challenging in the encryption image data retrieval. Towards this goal, this paper proposes a ciphertext image retrieval method based on SimHash in cloud computing. Firstly, we extract local feature of images, and then cluster the features by K-means. Based on it, the visual word codebook is introduced to represent feature information of images, which hashes the codebook to the corresponding fingerprint. Finally, the image feature vector is generated by SimHash searchable encryption feature algorithm for similarity retrieval. Extensive experiments on two public datasets validate the effectiveness of our method. Besides, the proposed method outperforms one popular searchable encryption, and the results are competitive to the state-of-the-art.

Keywords

Cloud computing, SimHash, encryption image retrieval, K-means.

Cite This Article

J. Qin, Y. Cao, X. Xiang, Y. Tan, L. Xiang et al., "An encrypted image retrieval method based on simhash in cloud computing," Computers, Materials & Continua, vol. 63, no.1, pp. 389–399, 2020.

Citations




This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2931

    View

  • 1823

    Download

  • 0

    Like

Related articles

Share Link

WeChat scan