Content-Based Multi-Source Encrypted Image Retrieval in Clouds with Privacy Preservation
Meng Shen, Guohua Cheng, Liehuang Zhu, Xiaojiang Du, Jiankun Hu

TL;DR
This paper introduces a privacy-preserving multi-source encrypted image retrieval scheme for cloud environments, enabling secure, accurate, and efficient content-based image search across multiple owners without compromising individual image privacy.
Contribution
It presents a novel secure CBIR scheme supporting multiple image owners using multi-party encryption, addressing privacy and efficiency challenges in cloud-based image retrieval.
Findings
Achieves high retrieval accuracy comparable to unencrypted methods
Ensures privacy of individual images among multiple owners
Demonstrates efficiency suitable for practical deployment
Abstract
Content-based image retrieval (CBIR) is one of the fundamental image retrieval primitives. Its applications can be found in various areas, such as art collections and medical diagnoses. With an increasing prevalence of cloud computing paradigm, image owners desire to outsource their images to cloud servers. In order to deal with the risk of privacy leakage of images, images are typically encrypted before they are outsourced to the cloud, which makes CBIR an extremely challenging task. Existing studies focus on the scenario with only a single image owner, leaving the problem of CBIR with multiple image sources (i.e., owners) unaddressed. In this paper, we propose a secure CBIR scheme that supports Multiple Image owners with Privacy Protection (MIPP). We encrypt image features with a secure multi-party computation technique, which allows image owners to encrypt image features with their…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Advanced Steganography and Watermarking Techniques · Image Retrieval and Classification Techniques
