Mobile edge computing (MEC) is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to re-duce network delay and improve the service quality of recommendation systems.In a MEC-based recom-mendation system,users\' rating data are collected and analyzed by the edge servers.If the servers behave dishonestly or break down,users\' privacy may be dis-closed.To solve this issue,we design a recommen-dation framework that applies local differential pri-vacy (LDP) to collaborative filtering.In the proposed framework,users\' rating data are perturbed to satisfy LDP and then released to the edge servers.The edge servers perform partial computing task by using the perturbed data.The cloud computing center computes the similarity between items by using the computing results generated by edge servers.We propose a data perturbation method to protect user\'s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation.And to enhance the protection of privacy,we propose two methods to protect both users\' rating values and rating behaviors.Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommen-dation methods.