ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (12): 2825-2832.doi: 10.7544/issn1000-1239.2017.20160647

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Trajectory Privacy Preserving Based on Statistical Differential Privacy

Zhu Weijun1, You Qingguang1, Yang Weidong2, Zhou Qinglei1   

  1. 1(School of Information Engineering, Zhengzhou University, Zhengzhou 450001); 2(College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001)
  • Online:2017-12-01

Abstract: With the continuous development of Internet of vehicles, Internet of vehicles provides the convenient services to drivers and passengers. But it also brings some new problems of privacy protection. The existing methods for trajectory data publishing may leak users' location privacy. Thus, it may endanger the users' personal safety. In order to avoid the drawbacks of adding random noise in the existing methods for differential privacy protection, we propose a novel method for trajectory privacy protection based on statistical differential privacy. At first, one can calculate the sensitivity of position nodes in vehicle traces according to the characteristics of traces since there are some characteristics of Markov process in vehicle traces. And then, one can add some moderate Laplace noises according to the sensitivity of position nodes, statistical threshold and sensitivity threshold. As a result, the new method is obtained. Evaluating the availability of the trajectory data through the average relative error, the experimental results verify the availability and effectiveness of the proposed approach for privacy preserving based on statistical differential privacy.

Key words: trajectory data, differential privacy, Markov process, data publishing, privacy protection

CLC Number: