A trajectory data publishing algorithm satisfying local suppression

Xiaohui Li,Yajun Wang,Bo Li,Yuliang Bai

doi:10.1177/1550147721993402

Xiaohui Li, Yajun Wang + Show 2 more

Open Access

https://doi.org/10.1177/1550147721993402

Copy DOI

Abstract

Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data. Therefore, we propose a trajectory data differential privacy protection algorithm based on local suppression Trajectory privacy protection based on local suppression (TPLS) to provide the user with the ability and flexibility of protecting data through local suppression. The main contributions of this article include as follows: (1) introducing privacy protection method in trajectory data release, (2) performing effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set, and (3) proposing a differential privacy protection algorithm based on local suppression. In the algorithm, we achieve the purpose Maximal frequent sequence (MFS) sequence loss rate in the trajectory data set by effective local inhibition judgment and updating the minimum violation sequence set, and then establish a classification tree and add noise to the leaf nodes to improve the security of the data to be published. Simulation results show that the proposed algorithm is effective, which can reduce the data loss rate and improve data availability while reducing the risk of user privacy leakage.

Highlights

With the development of positioning technology and the popularization of intelligent devices, a large number of trajectory data of moving objects are produced, and the information contained in these trajectory data makes the in-depth study and analysis of trajectory data becomes a research hot spot in the field of data mining.[1,2,3,4] Through the analysis and exploration of trajectory data, researchers obtain a large amount of valuable information to study the privacy protection of user information
We find that local suppression can effectively solve the problem of reducing data availability caused by global suppression, but it is possible to produce a new minimum violation sequence in the process of suppression, which may lead to the risk of data privacy disclosure again
In this article, we focus on improving the security of user trajectory data release process and reducing the degree of data loss due to global suppression or other ineffective local suppression and proposing a trajectory data differential privacy protection algorithm based on local suppression which satisfies the LKC-privacy model

Summary

Introduction

With the development of positioning technology and the popularization of intelligent devices, a large number of trajectory data of moving objects are produced, and the information contained in these trajectory data makes the in-depth study and analysis of trajectory data becomes a research hot spot in the field of data mining.[1,2,3,4] Through the analysis and exploration of trajectory data, researchers obtain a large amount of valuable information to study the privacy protection of user information. In order to ensure the security of user privacy information in trajectory data set while ensuring the availability of data to be published, this article proposes a trajectory data differential privacy protection algorithm based on local suppression.

Results

Conclusion