KPDR : An Effective Method of Privacy Protection
To solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of informat...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6674639 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566475385208832 |
|---|---|
| author | Zihao Shen Wei Zhen Pengfei Li Hui Wang Kun Liu Peiqian Liu |
| author_facet | Zihao Shen Wei Zhen Pengfei Li Hui Wang Kun Liu Peiqian Liu |
| author_sort | Zihao Shen |
| collection | DOAJ |
| description | To solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of information points is analyzed by using the characteristics of Dirichlet graph, and the anonymous set of users is generated and sent to LBS server. Second, the relationship matrix is generated, and the proximity relationship between the user position and the target information point is obtained by calculation. Then, the private information retrieval model is applied to ensure the privacy of users’ target information points. Finally, the experimental results show that the KPDR method not only satisfies the diversity of l3/4, but also increases the anonymous space, reduces the communication overhead, ensures the anonymous success rate of users, and effectively prevents the disclosure of user privacy. |
| format | Article |
| id | doaj-art-5316ce4f9661403db9722a815fea038b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5316ce4f9661403db9722a815fea038b2025-02-03T01:03:57ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66746396674639KPDR : An Effective Method of Privacy ProtectionZihao Shen0Wei Zhen1Pengfei Li2Hui Wang3Kun Liu4Peiqian Liu5School of Computer Science and Technology, Henan Polytechnic University, Jiao’zuo 454000, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiao’zuo 454000, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiao’zuo 454000, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiao’zuo 454000, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiao’zuo 454000, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiao’zuo 454000, ChinaTo solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of information points is analyzed by using the characteristics of Dirichlet graph, and the anonymous set of users is generated and sent to LBS server. Second, the relationship matrix is generated, and the proximity relationship between the user position and the target information point is obtained by calculation. Then, the private information retrieval model is applied to ensure the privacy of users’ target information points. Finally, the experimental results show that the KPDR method not only satisfies the diversity of l3/4, but also increases the anonymous space, reduces the communication overhead, ensures the anonymous success rate of users, and effectively prevents the disclosure of user privacy.http://dx.doi.org/10.1155/2021/6674639 |
| spellingShingle | Zihao Shen Wei Zhen Pengfei Li Hui Wang Kun Liu Peiqian Liu KPDR : An Effective Method of Privacy Protection Complexity |
| title | KPDR : An Effective Method of Privacy Protection |
| title_full | KPDR : An Effective Method of Privacy Protection |
| title_fullStr | KPDR : An Effective Method of Privacy Protection |
| title_full_unstemmed | KPDR : An Effective Method of Privacy Protection |
| title_short | KPDR : An Effective Method of Privacy Protection |
| title_sort | kpdr an effective method of privacy protection |
| url | http://dx.doi.org/10.1155/2021/6674639 |
| work_keys_str_mv | AT zihaoshen kpdraneffectivemethodofprivacyprotection AT weizhen kpdraneffectivemethodofprivacyprotection AT pengfeili kpdraneffectivemethodofprivacyprotection AT huiwang kpdraneffectivemethodofprivacyprotection AT kunliu kpdraneffectivemethodofprivacyprotection AT peiqianliu kpdraneffectivemethodofprivacyprotection |