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ARTICLE

Image Feature Computation in Encrypted Domain Based on Mean Value

Xiangshu Ou1, Mingfang Jiang2,*, Shuai Li1, Yao Bai1
1 Department of Mathematics and Computational Science, Hunan First Normal University, Changsha, 410205, China
2 Department of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China
* Corresponding Author: Mingfang Jiang. Email:

Journal of Cyber Security 2020, 2(3), 123-130. https://doi.org/10.32604/jcs.2020.09703

Received 15 January 2020; Accepted 23 July 2020; Issue published 14 September 2020

Abstract

In smart environments, more and more teaching data sources are uploaded to remote cloud centers which promote the development of the smart campus. The outsourcing of massive teaching data can reduce storage burden and computational cost, but causes some privacy concerns because those teaching data (especially personal image data) may contain personal private information. In this paper, a privacy-preserving image feature extraction algorithm is proposed by using mean value features. Clients use block scrambling and chaotic map to encrypt original images before uploading to the remote servers. Cloud servers can directly extract image mean value features from encrypted images. Experiments show the effectiveness and security of our algorithm. It can achieve information search over the encrypted images on the smart campus.

Keywords

Privacy-preserving; image encryption; cloud computing; mean value

Cite This Article

X. Ou, M. Jiang, S. Li and Y. Bai, "Image feature computation in encrypted domain based on mean value," Journal of Cyber Security, vol. 2, no.3, pp. 123–130, 2020.



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.
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