A Privacy-preserving Image Retrieval Method Based on Improved BoVW Model in Cloud Environment

Abstract

ABSTRACTWith the rapid development of cloud computing technology, more and more users choose to outsource image data to clouds. To protect users’ privacy and guarantee data’s confidentiality, images need to be encrypted before being outsourced to CSP, but this brings new difficulties to some basic yet important data services, such as content-based image retrieval (CBIR). In this paper, a privacy-preserving image retrieval method based on an improved BoVW model is proposed. An improved BoVW method based on Hamming embedding can provide binary signatures that refine the matching based on visual words; therefore, retrieval precision is improved significantly; orthogonal transformation is utilized to implement privacy-preserving image retrieval, where image features are divided into two different fields with orthogonal decomposition, for which encryption and distance comparison are executed separately, and two kinds of operation results are fused in the final vector with orthogonal composition. As a result, cloud server can extract components from encrypted features directly and compare distance with those of query image, without violating the privacy of images and features. Any algorithm can be used to encrypt features, which enhance the practicability of the proposed method. The security analysis and experimental results prove its security and retrieval performance.