Proper

Abstract

An important method of spatial-temporal data mining, trajectory clustering can mine valuable information in trajec¬tories. However, cluster results without special sanitization pose serious threats to individual location privacy. Existing privacy preserving mechanisms for trajectory clustering still contend with the problems of narrow applicability, low-level utility, and difficulty in being applied to real scenarios. In this paper, we therefore propose a differential privacy preserving mechanism, Cluster-Indistinguishability, to support trajectory clustering. Firstly, a general model of typical trajectory clustering algorithms is given, and the definition of differential privacy is introduced according to the model. Then, we derive the probability density function of two-dimensional Laplace noise, which satisfies the above definition. Finally, we transform the noise from a Carte¬sian coordinate system to a Polar coordinate system to efficiently apply it in real scenarios. Experimental results show that, Cluster-Indistinguishability has general applicability and better performance compared to existing methods.