Title: Privacy-preserving image retrieval based on additive secret sharing

Authors: Zhihua Xia; Qi Gu; Lizhi Xiong; Wenhao Zhou

Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

Abstract: The rapid growth of digital images motivates individuals to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would limit the efficient usage of images. Plenty of schemes on privacy-preserving content-based image retrieval (PPCBIR) tries to seek the balance between security and retrieval ability. However, compared to the works in content-based image retrieval (CBIR), the existing schemes are far deficient in both accuracy and efficiency. In this paper, inspired by additive secret sharing technology, we propose a series of secure computation protocols and show their application in PPCBIR. The experiments and security analysis demonstrate the efficiency, accuracy, and security of our scheme.

Keywords: privacy-preserving image retrieval; additive secret sharing; secure computation protocol; pre-trained CNN; secure CNN inference; secure PCA.

DOI: 10.1504/IJAACS.2024.137065

International Journal of Autonomous and Adaptive Communications Systems, 2024 Vol.17 No.2, pp.99 - 126

Received: 13 Dec 2021
Accepted: 06 Feb 2022
Published online: 01 Mar 2024 *

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