关键词: 轨迹, 隐私保护, (k, &delta, , l)-匿名模型, 时空位置点

Abstract: Trajectory data play an increasing important role on city planning, intelligent transportation and mobile business analysis, etc. However, publishing raw trajectory data will threat individuals’ privacy. The (k,δ)-anonymity is an effective model for trajectory privacy preservation, but it is vulnerable to spatio-temporal point linkage attack. To address the problem, this paper proposes a (k,δ,l)-anonymity model. The (k,δ,l)-anonymity model makes sure that each published trajectory is indistinguishable with at least other k-1 trajectories within a δ-radius cylinder, and each spatio-temporal point of the center trajectory of the cylinder is passed by at least l trajectories. This paper also proposes an AGG-NWA algorithm to implement the (k,δ,l)-anonymity model. The performance of the proposed (k,δ,l)-anonymity model is evaluated from two aspects: utility and security. The experimental results show that the proposed model can reserve similar utility as the (k,δ)-anonymity model, and outperforms the (k,δ)-anonymity model with respect to security.

Key words: trajectory, privacy preservation; (k,δ,l)-anonymity model, spatio-temporal point