Privacy-Preserving Socialized Recommendation based on Multi-View Clustering in a Cloud Environment
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918028206145536 |
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| author | Guo, Cheng Jia, Jing Wang, Peng Zhang, Jing |
| author_facet | Guo, Cheng Jia, Jing Wang, Peng Zhang, Jing |
| contents | Recommendation as a service has improved the quality of our lives and plays a significant role in variant aspects. However, the preference of users may reveal some sensitive information, so that the protection of privacy is required. In this paper, we propose a privacy-preserving, socialized, recommendation protocol that introduces information collected from online social networks to enhance the quality of the recommendation. The proposed scheme can calculate the similarity between users to determine their potential relationships and interests, and it also can protect the users' privacy from leaking to an untrusted third party. The security analysis and experimental results showed that our proposed scheme provides excellent performance and is feasible for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Socialized Recommendation based on Multi-View Clustering in a Cloud Environment Guo, Cheng Jia, Jing Wang, Peng Zhang, Jing Cryptography and Security Recommendation as a service has improved the quality of our lives and plays a significant role in variant aspects. However, the preference of users may reveal some sensitive information, so that the protection of privacy is required. In this paper, we propose a privacy-preserving, socialized, recommendation protocol that introduces information collected from online social networks to enhance the quality of the recommendation. The proposed scheme can calculate the similarity between users to determine their potential relationships and interests, and it also can protect the users' privacy from leaking to an untrusted third party. The security analysis and experimental results showed that our proposed scheme provides excellent performance and is feasible for real-world applications. |
| title | Privacy-Preserving Socialized Recommendation based on Multi-View Clustering in a Cloud Environment |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2505.15156 |