KPDR : An Effective Method of Privacy Protection

Zihao Shen, Wei Zhen, Pengfei Li, Hui Wang, Kun Liu, Peiqian Liu and Yongsheng Hao

Complexity, 2021, vol. 2021, 1-10

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/6674639.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/6674639.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6674639

DOI: 10.1155/2021/6674639

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().