KPDR : An Effective Method of Privacy Protection

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

doi:10.1155/2021/6674639

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 l 3 / 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.

Highlights

Complexity diversity on k-anonymity, which is vulnerable to continuous query attacks
To solve the above problems, this paper proposes a privacy protection method of K and P Dirichlet Retrieval (KPDR) based on Dirichlet graph model, which can protect users’ privacy from location and query
The private information retrieval (PIR) technology with relatively high security [13, 14] is adopted, which can ensure that the trusted third-party server (TTPS) can securely retrieve the desired data from the untrusted LBS server and effectively prevent the privacy disclosure caused by the attack of LBS

Summary

Introduction

Complexity diversity on k-anonymity, which is vulnerable to continuous query attacks. The private information retrieval (PIR) technology with relatively high security [13, 14] is adopted, which can ensure that the trusted third-party server (TTPS) can securely retrieve the desired data from the untrusted LBS server and effectively prevent the privacy disclosure caused by the attack of LBS. In the KPDR privacy protection method, the trusted third-party server and the LBS server jointly maintain a set of information points. E mobile terminal sends the query request information to the trusted third-party server, which generates Dirichlet graph according to the user query request information and its own cached information points and selects the false positions of the virtual user and the current user in K−1 D blocks according to the established rules to form a user anonymous set and send it to the LBS server. Where Sig stands for the unique name identification of the information point, Cla represents the category of the information point, and lat represents the coordinate information of the information point, and the introduction of information points is to enhance the ability to query and describe the user’s location and improve the query efficiency

Results

Discussion

Conclusion