Enhancing utility approach for user-centered location privacy service
When users use location-based services to obtain accurate recommendation results, the recommendation provider needs to obtain a large amount of user's located information. At this time, users worry about their information which be leaked to abandon service, and some users even upload dummy information. Owing to the problem that the privacy of users is easy to be leaked, we proposed a user-centered location recommendation services model based on differential privacy in this paper, which makes efficient recommendations while protecting the users' location privacy. Through controlling the expected utility error autonomously, not only the privacy of users can be guaranteed, but also service experience and the utility of expected utility error can be improved.