FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value
Abstract Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of pri...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01758-9 |
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| author | Weiwei Wang Wenping Ma Kun Yan |
| author_facet | Weiwei Wang Wenping Ma Kun Yan |
| author_sort | Weiwei Wang |
| collection | DOAJ |
| description | Abstract Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods. |
| format | Article |
| id | doaj-art-fe6406e02fd545b89f5ad44a54933b5e |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-fe6406e02fd545b89f5ad44a54933b5e2025-02-02T12:49:32ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111810.1007/s40747-024-01758-9FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley valueWeiwei Wang0Wenping Ma1Kun Yan2School of Telecommunication Engineering, Xidian UniversitySchool of Telecommunication Engineering, Xidian UniversitySchool of Telecommunication Engineering, Xidian UniversityAbstract Collaborative filtering recommendation systems generate personalized recommendation results by analyzing and collaboratively processing a large numerous of user ratings or behavior data. The widespread use of recommendation systems in daily decision-making also brings potential risks of privacy leakage. Recent literature predominantly employs differential privacy to achieve privacy protection, however, many schemes struggle to balance user privacy and recommendation performance effectively. In this work, we present a practical privacy-preserving scheme for user-based collaborative filtering recommendation that utilizes fuzzy C-means clustering and Shapley value, FSPPCFs, aiming to enhance the recommendation performance while ensuring privacy protection. Specifically, (i) we have modified the traditional recommendation scheme by introducing a similarity balance factor integrated into the Pearson similarity algorithm, enhancing recommendation system performance; (ii) FSPPCFs first clusters the dataset through fuzzy C-means clustering and Shapley value, grouping users with similar interests and attributes into the same cluster, thereby providing more accurate data support for recommendations. Then, differential privacy is used to achieve the user’s personal privacy protection when selecting the neighbor set from the target cluster. Finally, it is theoretically proved that our scheme satisfies differential privacy. Experimental results illustrate that our scheme significantly outperforms existing methods.https://doi.org/10.1007/s40747-024-01758-9Privacy protectionCollaborative filteringFuzzy C-means clusteringShapley valueRecommendation system |
| spellingShingle | Weiwei Wang Wenping Ma Kun Yan FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value Complex & Intelligent Systems Privacy protection Collaborative filtering Fuzzy C-means clustering Shapley value Recommendation system |
| title | FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value |
| title_full | FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value |
| title_fullStr | FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value |
| title_full_unstemmed | FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value |
| title_short | FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value |
| title_sort | fsppcfs a privacy preserving collaborative filtering recommendation scheme based on fuzzy c means and shapley value |
| topic | Privacy protection Collaborative filtering Fuzzy C-means clustering Shapley value Recommendation system |
| url | https://doi.org/10.1007/s40747-024-01758-9 |
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