Accelerating Privacy-Preserving Image Retrieval with Multi-Index Hashing

Abstract

With the explosive growth of data, a large amount of image data is stored on cloud servers. However, cloud servers can easily collect sensitive information about stored images, which brings serious privacy issues. Although uploading encrypted images to cloud servers could solve the privacy problem, most of the existing privacy-preserving schemes inevitably reduce the accuracy and efficiency of image retrieval. To address the above challenging issues, we propose a privacy-preserving content-based image retrieval scheme based on multi-indexed hashing (MIH) in this paper. To improve the retrieval precision, the ViT model is first used to extract feature descriptors of images and ITQ method is utilized to downscale the feature vectors into binary vectors. Subsequently, based on additive secret sharing, we propose a new secure Hamming distance calculation protocol to perform similarity measure, which protects the data privacy of image features. Finally, we design a secure multi-index hash structure to filter the dataset to improve the search efficiency. Experiments on the dataset demonstrate the efficiency and security of the scheme.