MSPPIR: Multi-source privacy-preserving image retrieval in cloud computing
Qi Gu, Zhihua Xia, Xingming Sun

TL;DR
This paper introduces JES-MSIR, a novel encryption scheme for multi-source privacy-preserving image retrieval in cloud computing, supporting secure, efficient, and accurate retrieval from multiple encrypted sources.
Contribution
It proposes JES-MSIR, the first scheme tailored for multi-source privacy-preserving image retrieval using JPEG encryption, addressing practical multi-source scenarios.
Findings
Supports secure retrieval from multiple sources
Demonstrates efficiency and security through experiments
Achieves accurate retrieval with encrypted images
Abstract
Content-Based Image Retrieval (CBIR) techniques have been widely researched and in service with the help of cloud computing like Google Images. However, the images always contain rich sensitive information. In this case, the privacy protection become a big problem as the cloud always can't be fully trusted. Many privacy-preserving image retrieval schemes have been proposed, in which the image owner can upload the encrypted images to the cloud, and the owner himself or the authorized user can execute the secure retrieval with the help of cloud. Nevertheless, few existing researches notice the multi-source scene which is more practical. In this paper, we analyze the difficulties in Multi-Source Privacy-Preserving Image Retrieval (MSPPIR). Then we use the image in JPEG-format as the example, to propose a scheme called JES-MSIR, namely a novel JPEG image Encryption Scheme which is made for…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Advanced Image Fusion Techniques
