FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing
With the rapid deployment of electronic imaging devices, plenty of high-quality images are in the general public’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....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9796537/ |
| _version_ | 1811333667714236416 |
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| author | Lei Zhang Ruiyan Xia Wensheng Tian Zhaokun Cheng Zhichao Yan Panpan Tang |
| author_facet | Lei Zhang Ruiyan Xia Wensheng Tian Zhaokun Cheng Zhichao Yan Panpan Tang |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | With the rapid deployment of electronic imaging devices, plenty of high-quality images are in the general public’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’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. |
| first_indexed | 2024-04-13T16:56:23Z |
| format | Article |
| id | doaj.art-eea37822b5f6427eb0e1691a0455dc4f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T16:56:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eea37822b5f6427eb0e1691a0455dc4f2022-12-22T02:38:48ZengIEEEIEEE Access2169-35362022-01-0110640286404210.1109/ACCESS.2022.31832249796537FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret SharingLei Zhang0Ruiyan Xia1Wensheng Tian2https://orcid.org/0000-0002-6069-1646Zhaokun Cheng3https://orcid.org/0000-0002-8362-1616Zhichao Yan4Panpan Tang5Nanhu Laboratory, Jiaxing, ChinaSchool of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaNanhu Laboratory, Jiaxing, ChinaNanhu Laboratory, Jiaxing, ChinaNanhu Laboratory, Jiaxing, ChinaNanhu Laboratory, Jiaxing, ChinaWith the rapid deployment of electronic imaging devices, plenty of high-quality images are in the general public’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’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.https://ieeexplore.ieee.org/document/9796537/Secure image retrievalfederated learningadditive secret sharingsecure comparison protocol |
| spellingShingle | Lei Zhang Ruiyan Xia Wensheng Tian Zhaokun Cheng Zhichao Yan Panpan Tang FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing IEEE Access Secure image retrieval federated learning additive secret sharing secure comparison protocol |
| title | FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing |
| title_full | FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing |
| title_fullStr | FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing |
| title_full_unstemmed | FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing |
| title_short | FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing |
| title_sort | flsir secure image retrieval based on federated learning and additive secret sharing |
| topic | Secure image retrieval federated learning additive secret sharing secure comparison protocol |
| url | https://ieeexplore.ieee.org/document/9796537/ |
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