Open Access

ARTICLE

A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

Shiqi Wang¹, Mingfang Jiang^2,*, Jiaohua Qin¹, Hengfu Yang², Zhichen Gao³

1 College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China
2 Department of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China
3 Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, NY, 11794, USA

* Corresponding Author: Mingfang Jiang. Email:

Computers, Materials & Continua 2021, 68(3), 2979-2993. https://doi.org/10.32604/cmc.2021.017094

Received 20 January 2021; Accepted 04 March 2021; Issue published 06 May 2021

Abstract

In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content, original images are encrypted using block scrambling, pixel circular shift, and pixel diffusion when uploaded to the cloud server. It is proved that RILBP features remain unchanged before and after encryption. Moreover, the server can directly extract RILBP features from encrypted images. Analyses and experiments confirm that the proposed scheme is secure and effective, and outperforms previous secure LBP feature computing methods.

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Citations