Personalized privacy protection method for group recommendation
To address the problem that most of the existing privacy protection methods can not satisfy the user’s personalized requirements very well in group recommendation,a user personalized privacy protection framework based on trusted client for group recommendation (UPPPF-TC-GR) followed with a group sen...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2019-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019183/ |
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| _version_ | 1850095602622267392 |
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| author | Haiyan WANG Jinxiang LU |
| author_facet | Haiyan WANG Jinxiang LU |
| author_sort | Haiyan WANG |
| collection | DOAJ |
| description | To address the problem that most of the existing privacy protection methods can not satisfy the user’s personalized requirements very well in group recommendation,a user personalized privacy protection framework based on trusted client for group recommendation (UPPPF-TC-GR) followed with a group sensitive preference protection method (GSPPM) was proposed.In GSPPM,user’s historical data and privacy preference demands were collected in the trusted client,and similar users were selected in the group based on sensitive topic similarity between users.Privacy protection for users who had privacy preferences in the group was realized by randomization of cooperative disturbance to top k similar users.Simulation experiments show that the proposed GSPPM can not only satisfy privacy protection requirements for each user but also achieve better performance. |
| format | Article |
| id | doaj-art-ef92ea02c8eb4ff88c1472484d2da93d |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-ef92ea02c8eb4ff88c1472484d2da93d2025-08-20T02:41:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-09-014010611559729677Personalized privacy protection method for group recommendationHaiyan WANGJinxiang LUTo address the problem that most of the existing privacy protection methods can not satisfy the user’s personalized requirements very well in group recommendation,a user personalized privacy protection framework based on trusted client for group recommendation (UPPPF-TC-GR) followed with a group sensitive preference protection method (GSPPM) was proposed.In GSPPM,user’s historical data and privacy preference demands were collected in the trusted client,and similar users were selected in the group based on sensitive topic similarity between users.Privacy protection for users who had privacy preferences in the group was realized by randomization of cooperative disturbance to top k similar users.Simulation experiments show that the proposed GSPPM can not only satisfy privacy protection requirements for each user but also achieve better performance.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019183/group recommendationpersonalized privacy protectionrandomized perturbationk-anonymous |
| spellingShingle | Haiyan WANG Jinxiang LU Personalized privacy protection method for group recommendation Tongxin xuebao group recommendation personalized privacy protection randomized perturbation k-anonymous |
| title | Personalized privacy protection method for group recommendation |
| title_full | Personalized privacy protection method for group recommendation |
| title_fullStr | Personalized privacy protection method for group recommendation |
| title_full_unstemmed | Personalized privacy protection method for group recommendation |
| title_short | Personalized privacy protection method for group recommendation |
| title_sort | personalized privacy protection method for group recommendation |
| topic | group recommendation personalized privacy protection randomized perturbation k-anonymous |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019183/ |
| work_keys_str_mv | AT haiyanwang personalizedprivacyprotectionmethodforgrouprecommendation AT jinxianglu personalizedprivacyprotectionmethodforgrouprecommendation |