Now many applications of trajectory (location) data have facilitated people's daily life. However, publishing trajectory data may divulge individual sensitive information so as to influence people's normal life. On the other hand, if we cannot mine and share trajectory data information, trajectory data will lose its value to serve our society. Currently, because the records of trajectory data are 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 Laplace's differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user's trajectory data; secondly, the algorithm builds the polygons according to the protected points and the adjacent and high frequent accessed points selected from the accessed point database, then the algorithm calculates the polygon centroids; finally, the noises are added to the polygon centroids by the Laplace's differential privacy method, and the new polygon centroids are used to 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. Povzetek: Predlagana je metoda za učinkovito varovanje podatkov o poteh na osnovi Laplacove diferenčne privatnosti.
Besides, Gu et al. REF designed a trajectory data privacy protection scheme based on Laplace's differential privacy mechanism, in which the noise is added to the polygon centroid by the Laplace's differential privacy method, and then the new polygon centroid is used to replace the protected points, and finally the algorithm constructs and issues the new trajectory data.
Efficient Trajectory Data Privacy Protection Scheme Based on Laplace's Differential Privacy
Informatica
Informatica (Slovenia)
Computer Science