Recent　years　have　witnessed　the　rapid　development　of　cloud　computing,　which　leverages　its　unique　services　to　cloud　customers　in　a　pay-as-you-go,　anytime,　anywhere　manner.　However,　a　significant　barrier　to　the　adoption　of　cloud　services　is　that　users　fear　data　leakage　and　loss　of　privacy　if　their　sensitive　data　is　processed　in　the　cloud.　Hence,　the　cloud　customer　must　be　able　to　select　appropriate　services　according　to　his　or　her　privacy　and　security　needs.　In　this　paper,　we　propose　a　novel　cloud　service　selection　method　called　PCSS,　where　a　cloud　service　is　estimated　based　on　its　capability　of　privacy　protection　(CoPP)　covering　the　entire　life-cycle　of　users＇　data.　A　scalable　assessment　index　system　with　a　2-level　hierarchy　structure　is　constructed　to　analyze　and　quantify　the　CoPP　of　cloud　service.　The　first-level　index　is　composed　of　all　stages　of　data　life-cycle　and　the　second-level　index　involves　privacy-aware　security　mechanisms　at　each　stage.　We　employ　a　fuzzy　comprehensive　evaluation　technique　to　count　the　privacy-preserving　value　of　security　mechanism.　An　AHP-　based　approach　is　exploited　to　decide　the　impact　weight　of　different　security　mechanisms　to　the　CoPP　of　each　stage.　By　calculating　a　comprehensive　CoPP　metric　of　all　life-cycle　stages,　all　cloud　services　can　be　sorted　and　recommended　to　users.　An　example　analysis　is　given,　and　the　reasonableness　of　the　proposed　method　is　proved.　Comprehensive　experiments　have　been　conducted,　which　demonstrate　the　effectiveness　of　the　proposed　method　by　the　comparison　with　the　baseline　method　at　the　service　selection　performance.　©　2014　IEEE.