In this paper we propose a novel location-privacy preserving mechanism for Location based services. To take advantage of the high effectiveness of hiding user queries from the server, which minimizes the exposed information about the users’ location to the server; we propose a mechanism in which a user can hide in the mobile crowd while using the service. MobiCrowd would be used exactly where it is most effective. We evaluate MobiCrowd through both an epidemic- based differential equation model and a Bayesian frame- work for location inference attacks. The epidemic model is a novel approach to evaluating a distributed location-privacy protocol. It helps us analyze how the parameters of our scheme, combined with a time dependent model of the users’ mobility, could cause a high or low-degree privacy. We validate the model based results (on the probability of hiding a user from the server) with simulations on real mobility traces. We find that our epidemic model is a very good approximation of the real protocol; it reflects the precise hiding probability of a user.