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• W4285058051 abstract "With the rapid deployment of electronic imaging devices, plenty of high-quality images are in the general public&#x2019;s hands. These images can be profitable, such as providing retrieval services; however, it is difficult for the individual to profit without the support of the cloud platform. The straightforward idea is that image owners upload their images to the cloud; yet, it is infeasible as the cloud platforms are not fully-trusted. In previous works, in order to protect the privacy of image owners, many researchers consider the Secure content-based Image Retrieval (SIR) task, which enables cloud servers to provide retrieval services while not exposing the images from the owners. However, the existing schemes are often not friendly to users as it&#x2019;s assumed that the owners have no profit demand and are unwilling to provide extra computation resources. This work introduces federated learning into SIR, which ensures better retrieval accuracy and efficiency; the additive secret sharing technology is utilized to protect the image information, and a better secure comparison protocol is proposed for better efficiency. We believe that the users can enjoy a better secure retrieval service with our proposed scheme. The experiment results and security analysis demonstrate that our scheme provides a significant accuracy advantage while ensuring efficiency and security." @default.
• W4285058051 created "2022-07-13" @default.
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• W4285058051 date "2022-01-01" @default.
• W4285058051 modified "2023-10-17" @default.
• W4285058051 title "FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing" @default.
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• W4285058051 doi "https://doi.org/10.1109/access.2022.3183224" @default.
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