Trajectory data privacy protection method based on trajectory segmentation

Abstract

The invention discloses a privacy protection method for track data release based on track segmentation. The privacy protection method comprises the following steps: applying an equivalence class division algorithm based on track segmentation filling to an original track data set accumulated by a location-based service application provider; applying a clustering group construction algorithm based on trajectory segmentation clustering to each equivalence class; determining the starting time of a clustering group and performing dividing to obtain candidate clustering groups; traversing each candidate clustering group, determining a trajectory set, and constructing trajectory segments outside a clustering group time interval; inserting the tracks which are not added into the clustering group into the clustering group with the same time interval and the nearest spatial position; and performing spatial disturbance on each position point on each track in each clustering group, and changing each clustering group into an anonymous track set as a track data set which can be directly published. According to the method, an equivalence class division algorithm is adopted, the number of deletedspace-time points is reduced, it is guaranteed that the equivalence class contains enough tracks, and the availability of the to-be-published data is improved.

Description

technical field [0001] The invention relates to a privacy protection technology for release of trajectory data, in particular to a trajectory data privacy protection method based on trajectory segmentation. Background technique [0002] Trajectory data refers to the data containing the user's time and location sequence information. With the popularity of terminal devices with positioning functions such as mobile phones and vehicle sensors, users' spatiotemporal data are more and more easily collected, resulting in a large amount of trajectory data. Trajectory data analysis has important application value for governments and enterprises, and has broad application prospects in traffic monitoring, urban planning, mobility management, and marketing strategies. In real life, many research institutions or academic organizations will release these trajectory data to the outside world for the purpose of decision-making and scientific research. However, this information is highly se...

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Problems solved by technology

[0004] Aiming at the deficiencies in the clustering group construction algorithm in the prior art, such as divergent trajectory distribution in the clustering group and low data availability due to th

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