chapter  14
30 Pages

Anonymizing Trajectory Data

Location-aware devices are used extensively in many network systems, such as mass transportation, car navigation, and healthcare management. The collected spatio-temporal data capture the detailed movement information of the tagged objects, offering tremendous opportunities for mining useful knowledge. Yet, publishing the raw data for data mining would reveal specific sensitive information of the tagged objects or individuals. In this chapter, we study the privacy threats in trajectory data publishing and show that traditional anonymization methods are not applicable for trajectory data due to its challenging properties: high-dimensional, sparse, and sequential. In this chapter, we study several trajectory data anonymization methods to address the anonymization problem for trajectory data.