A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing - Xia, Zhihua; Wang, Xinhui; Zhang, Liangao; Qin, Zhan; Sun, Xingming; Ren, Kui (IEEE transactions on information forensics and security, 2016)
With the increasing importance of images in people's daily life, content-based image retrieval (CBIR) has been widely studied. Compared with text documents, images consume much more storage space. Hence, its maintenance is considered to be a typical example for cloud storage outsourcing. For privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before outsourcing, which makes the CBIR technologies in plaintext domain to be unusable. In this paper, we propose a scheme that supports CBIR over encrypted images without leaking the sensitive information to the cloud server. First, feature vectors are extracted to represent the corresponding images. After that, the pre-filter tables are constructed by locality-sensitive hashing to increase search efficiency. Moreover, the feature vectors are protected by the secure kNN algorithm, and image pixels are encrypted by a standard stream cipher. In addition, considering the case that the authorized query users may illegally copy and distribute the retrieved images to someone unauthorized, we propose a watermark-based protocol to deter such illegal distributions. In our watermark-based protocol, a unique watermark is directly embedded into the encrypted images by the cloud server before images are sent to the query user. Hence, when image copy is found, the unlawful query user who distributed the image can be traced by the watermark extraction. The security analysis and the experiments show the security and efficiency of the proposed scheme.