Efficient Trajectory Data Privacy Protection Scheme Based on Laplace's Differential Privacy

Ke Gu, Lihao Yang, Yongzhi Liu, Bo Yin


Now many applications of location data have facilitated people’s daily life. However, publishing location data may divulge individual sensitive information so as to influence people’s normal life. On the other hand, if we cannot mine and share location data information, location data will lose its value to serve our society. Currently, as the records about trajectory data may be discrete in database, some existing privacy protection schemes are difficult to protect trajectory data. In this paper, we propose a trajectory data privacy protection scheme based on differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user’s trajectory data; secondly, the algorithm forms the polygon according to the protected points and the adjacent and high frequent accessed points that are selected from the accessing point database, then the algorithm calculates the polygon centroids; finally, the noises are added to the polygon centroids by the differential privacy method, and the polygon centroids replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher.

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DOI: https://doi.org/10.31449/inf.v42i3.1638

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