CMOA: continuous moving object anonymization

Abstract

This paper proposes a continuous anonymization method for a trajectory stream. In today's mobile environment, positions of moving objects are frequently sensed and collected. For real-time movement pattern analyses of people and automobiles, trajectory streams have attracted a lot of attention. Trajectory streams lead to sensitive locations, such as homes and personal hospitals. Additionally, a set of spatio-temporal data might identify a user from a trajectory stream. Therefore, publishing original trajectory streams may cause critical breaches of privacy. To protect privacy of users, we need a mechanism which makes it difficult to identify users from crowds of trajectory streams. Several techniques for anonymizing trajectories have been proposed. Anonymized trajectories can be published without concerning about privacy issues. However, for the continuous publishing of trajectory streams, existing trajectory anonymization methods are not suitable because they anonymize the overall trajectories at a time. If the existing methods are applied in the continuous publishing, the resolution of anonymized trajectory is hugely degraded or trace-ability is lost. In this paper, we propose an anonymization technique for a trajectory stream. The method continuously anonymizes trajectory streams one by one, and dynamically reforms anonymized trajectory streams to improve the resolution. The experiments showed that our method could keep the resolution at a constant level.