Point-of-interests (POIs) recommendation technology using user’s check-in data has attracted great attentions in recent years. However, user’s check-in data often contains sensitive information such as time and location data. Due to privacy considerations, many users are unwilling to share their check-in data with untrusted service providers, which has a great negative impact on recommendation quality. Trying to solve this problem, geographical and social society attributes based privacy preserving recommendation method for POIs, named GSSA-PPRM, is proposed in the paper. In the proposed method, a local differentially private matrix factorization algorithm is firstly designed to learn user’s preference with social attribute in client/server style. Then, according to the learned preference and considering geographical distance of POIs, a self-adaptive kernel density estimation algorithm is devised to study user’s check-in behavior. And an algorithm that tallies POI visit count and computes POI popularity by securely collecting user’s check-in data through random response (RR) mechanism is presented. Finally, a rating rule is given to predict the ratings of users for POIs by integrating kernel density estimation and POI popularity. The experimental results on two real datasets validate that the proposed method achieves better POI recommendation quality in condition of keeping user’s privacy.

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