Location Recommendation with Privacy Protection

With the development of Internet technology, users pay more and more attention to the privacy of personal location data. In order to cover up the user's original check-in data information and prevent attackers from using the user's friend relationship to infer the privacy information of a single user, our paper proposed a hybrid privacy protection method based on differential privacy and random perturbation, and combined the user's friend relationship to realize the location recommendation with privacy protection. Data analysis shows that the privacy level can be set by adding different degrees of random noise to achieve the purpose of personalized privacy protection. Furthermore, differential privacy is used to protect the user's friend relationship, which makes the privacy protection effect of the location recommendation method better. Experiments on real datasets, show that this method can protect users' privacy information and at the same time have a certain accuracy of location recommendation.