Low-Information-Loss Anonymization of Trajectory Data Considering Map Information

Preserving an individual's privacy when publishing data are essential, and anonymization has been getting attention as the solution. When anonymizing data, it is necessary to contemplate the possibilities of linkage with other data which can lead to privacy violation. Trajectory data are one of the data, which contains personal data. Consequently, various anonymization methods of trajectory data have been considered by researchers. However, most research handle trajectory data as polylines connecting location data or as a sequence of location data. In other words, it lacks on considering the connection with map data. In this paper, we will consider the anonymization of trajectory data of moving users matched according to map data, which we will be calling pathing data. According to k-anonymity principle, data can be published if there are k of the same data. We will use k-anonymity principle to quantitively judge the risk of privacy violation and propose two methods that can fulfill the anonymization requirements with low data loss. The two methods are Map Matching to Node (MMtoN) and Map matching to Edge (MMtoE), which judges k-anonymity by segments of pathing data.