Track clustering privacy protection method based on semantics

Abstract

The invention discloses a track clustering privacy protection method based on semantics. Vehicle track data mining and privacy protection of interest points in a track are combined. The method comprises the following steps: firstly, carrying out trajectory segmentation by using an improved Clusters function so as to generate a cluster on a single trajectory; then calculating a distance range Eps of trajectory data clustering; determining an Eps neighborhood of the track point according to the Eps, then determining a core point of the cluster, and performing stop area clustering by using the core point and the Eps neighborhood of the core point; adjusting the Eps to enable the Eps to meet a termination condition; performing secondary clustering on the stop area through core attribute selection; before track data is published, carrying out road network segmentation according to a privacy protection level and a Delaunay triangulation method to form a plurality of Voronoi maps (VM); secondly, based on the anonymity degree and the category degree, classifying the interest points in each VM into a plurality of buckets (buckets); and finally, publishing track data containing the pseudo positions of the interest points. In the invention, the pseudo positions of the interest points refer to the interest points closest to the duration time of each interest point in the bucket corresponding to the interest point. According to the method, the privacy information involved in the vehicle track interest point mining process can be effectively protected.

Description

Technical field[0001]The present invention relates to the field of data mining and privacy protection, specifically a semantic trajectory cluster privacy method.Background technique[0002]With the widespread application of GPS equipment, the rapid increase in vehicle trajectory data, from the complex time and space trajectory, it has become an important issue. Traditional trajectory clustering methods mainly include distance measurements, density-based methods and hierarchical methods, etc., these methods are to be further improved in terms of clustering accuracy. The clustering method based on semantic analysis in recent years effectively improves clustering accuracy, but there is still no privacy protection in the process of clustering. Therefore, how to protect the privacy data without leakage while performing data mining, it is an urgent problem to solve.Inventive content[0003]It is an object of the present invention to provide a semantic-based trajectory cluster privacy method t...

AI Technical Summary

Problems solved by technology

The clustering method based on semantic analysis proposed in recent years has effectively improved the clustering accuracy, but in the process of clustering, privacy protection is still not considered

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