ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (3): 576-584.doi: 10.7544/issn1000-1239.2019.20180033

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Trajectory Privacy Protection Method Based on Multi-Anonymizer

Zhang Shaobo1, Wang Guojun2, Liu Qin3, Liu Jianxun1   

  1. 1(School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201); 2(School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006); 3(College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082)
  • Online:2019-03-01

Abstract: At present, trajectory privacy protection in continuous location-based services has attracted wide attention. Some scholars have proposed some privacy-preserving methods, which mainly adopt the centralized structure based on the trusted third-party. However, there are privacy risks and performance bottlenecks in this structure. To overcome these defects, a trajectory privacy-preserving method based on multi-anonymizer (TPMA) is proposed by deploying multiple anonymizers between the user and the location service provider. In each query the user first selects a pseudonym, and the user’s query content is divided into n shares by the Shamir threshold scheme. Further, they are sent to n different anonymizers that randomly selected for processing, and one of the anonymizers is responsible for the user’s K-anonymity. In this method, the attacker cannot obtain the user’s trajectory and query content from a single anonymizer, and the anonymizer can be semi-trusted entity. The method can enhance the privacy of the user’s trajectory and can effectively solve the single point failure and the performance bottleneck in a single anonymizer structure. Security analysis shows that our approach can effectively protect the user’s trajectory privacy. Experiments show this method can reduce the computation and communication overhead of the single anonymizer compared with the trusted third party model.

Key words: location-based service, trajectory privacy, multi-anonymizer, Shamir threshold, pseudonym

CLC Number: