Traditional collaborative filtering (CF) methods suffer from sparse or even cold-start problems, especially for new established recommenders. However, since there are now quite a few recommender systems already existing in good working order, their data should be valuable to the new-start recommenders. This paper proposes shared collaborative filtering approach to leverage the data from other parties (contributor party) to improve own (beneficiary party's) CF performance, and at the same time the privacy of other parties cannot be compromised. Item neighborhood list is chosen as the shared data from the contributor party with considering differential privacy. And an innovative algorithm called neighborhood boosting is proposed to make the beneficiary party leverage the shared data. MovieLens and Netflix data sets are considered as two parties to simulate and evaluate the proposed shared CF approach. The experiment results validate the positive effects of shared CF for increasing the recommendation accuracy of the beneficiary party. Especially when the beneficiary party's data is quite sparse, the performance can be increased by around 10%. The experiments also show that shared CF even outperforms the methods that incorporate the detailed original rating scores of the contributor party without considering the privacy issues. The proposed shared CF approach obtains a win-win situation for both performance and privacy.