Collaborative filtering(CF), as a technique that automatically predicts the interest of an user by collecting rating information from other similar users or items, has been widely deployed in various recommendation systems. However, CF prediction is based on the users' historical ratings, indicating that it may reflect some of the users' private preferences. Consequently, enhancing CF model with privacy preservation guarantee has attracted much research attention. In this paper, we propose three privacy preserving collaborative filtering algorithms: DPSS++, DPSAObj++ and DPSAOut++. All the algorithms are based on SVD++, one of the most used CF algorithms, and differential privacy model, one the most important advance in the area of privacy preserving in the last decade. Our analysis shows that the proposed algorithms not only can provide reliable guarantee in terms of privacy preserving, but also keep high prediction accuracy.