Privacy-Preserving Image Retrieval Based on Additive Secret Sharing

TL;DR

This paper introduces a privacy-preserving image retrieval method using additive secret sharing, enabling secure, accurate, and efficient content-based image search in cloud environments with multiple servers.

Contribution

It presents novel additive secure computing protocols applied to CNN-based image retrieval, improving security, efficiency, and accuracy over existing schemes.

Findings

The proposed scheme achieves high retrieval accuracy.

It demonstrates improved efficiency in secure image processing.

The security analysis confirms robustness against potential attacks.

Abstract

The rapid growth of digital images motivates individuals and organizations to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would strongly limit the efficient usage of images. Plenty of existing schemes on privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the balance between security and retrieval ability. However, compared to the advanced technologies in CBIR like Convolutional Neural Network (CNN), the existing PPCBIR schemes are far deficient in both accuracy and efficiency. With more cloud service providers, the collaborative secure image retrieval service provided by multiple cloud servers becomes possible. In this paper, inspired by additive secret sharing technology, we propose a series of additive secure computing protocols on numbers and matrices with better efficiency, and then show their application in PPCBIR. Specifically, we extract CNN features, decrease the dimension of features and build the index securely with the help of our protocols, which include the full process of image retrieval in the plaintext domain. The experiments and security analysis demonstrate the efficiency, accuracy, and security of our scheme.