Collaborative　fltering　is　now　successfully　applied　to　recommender　systems.　The　availability　of　extensive　personal　data　is　necessary　for　generating　high　quality　recommendations.　However,　traditional　collaborative　fltering　methods　suffer　from　sparse　and　sometimes　cold-start　problems,　particularly　for　newly　deployed　recommenders.　Currently,　several　recommender　systems　exist　in　good　working　order,　and　data　collected　from　these　existing　systems　should　be　valuable　for　newly　deployed　recommenders.　This　paper　introduces　a　privacy　preserving　shared　collaborative　fltering　problem　in　order　to　leverage　the　data　from　other　parties　(contributors)　to　improve　its　own　(benefciaries)　collaborative　fltering　performance,　with　the　privacy　protected　under　a　differential　privacy　framework.　It　proposes　a　two-step　methodology　to　solve　this　problem.　First,　item-based　neighborhood　information　is　selected　as　the　shared　data　from　the　contributor　with　guaranteed　differential　privacy,　and　a　practical　enforcement　mechanism　for　differential　privacy　is　proposed.　Second,　two　novel　algorithms　are　developed　to　enable　the　beneficiary　to　leverage　the　shared　data　to　support　improved　collaborative　fltering.　The　extensive　experimental　results　show　that　the　proposed　methodology　can　increase　the　recommendation　accuracy　of　the　benefciary　significantly　while　preserving　data　privacy　for　the　contributors.