A model on dynamical anonymous area estimation based on Bayesian network

This paper presents a model for location anonymous area estimation, which establishes the related Bayesian inference network by making full use of the time-varying characteristic of person traffic or vehicle traffic, thus implementing the dynamical estimation method of minimum anonymous area. Integrating the real data, the method makes the prediction, therefore it is more accurate. The model in this paper further solves the location anonymous area estimation problem, which is the key issue balancing between location privacy protection and location service quality. The simulated experiments show that the method shortens the service response time to a great degree since it provides real time, moderate anonymous area suggestion on the premise of guaranteeing location privacy protection and query privacy protection, thus improving the location service quality.