Privacy-Preserving Image Classification and Retrieval Scheme over Encrypted Images

Image retrieval is a crucial function in several emerging computer vision applications, including online medical diagnosis and image recognition systems. With an increasing number of images being generated and outsourced to public clouds, there is a growing concern about privacy leaks of image contents. To address this issue, we propose an efficient privacy-preserving image classification and retrieval scheme (PICR) that employs low-dimensional vectors to represent image categories and feature vectors, thereby improving retrieval efficiency and reducing index storage costs. First, we design a feature extraction model based on convolutional neural network (CNN) to generate segmented hash codes that represent both image categories and features. Next, the cryptographic hierarchical index structure based on the category hash code is designed to improve retrieval accuracy and efficiency. Then, we employ random vectors and the Learning With Errors (LWE)-based secure k-Nearest Neighbour (kNN) algorithm to preserve the privacy of segmented hash codes and file-access patterns. Finally, we provide the security analysis that verifies our PICR scheme can protect image privacy as well as indexing and query privacy. Experimental evaluation demonstrates that our proposed scheme outperforms the existing state-of-the-art schemes in terms of retrieval accuracy, search efficiency and storage costs.