The　fast　development　of　cloud　technology　has　brought　about　a　new　trend　in　the　field　of　information　service:　more　and　more　information　is　being　transferred　to　the　cloud　as　requested.　However,　the　data,　such　as　texts,　images,　sounds,　and　videos,　before　being　moved　to　the　cloud,　in　most　cases,　has　to　be　encrypted　so　that　intelligible　information　will　not　be　obtained　from　unauthorized　accesses.　While　having　done　a　nice　work　in　protecting　the　data　privacy　of　its　owners,　this　encrypting　process,　has　produced　a　great　challenge　for　retrieval　of　the　document　stored　via　traditional　IR　model　based　on　document,　query　and　relevance.　In　order　to　retrieve　encrypted　information　from　cloud,　an　alternative　retrieval　system　is　needed.　To　satisfy　such　a　need,　we　have:　1)　build　a　cloud　information　retrieval　framework　characterized　by　its　retrieval　risk　formula,　which,　enables,　for　the　very　first　time　to　the　best　of　our　knowledge,　an　effective　retrieval　of　keywords　from　encrypted　cloud　data　without　undermining　key　word　privacy　and　retrieval　performance;　and　2)　upgraded　the　existing　searchable　encryption　scheme　that　can　only　support　simple　equality　queries　on　encrypted　data　and　has　been　proved　to　perform　slightly　better　than　random　selection,　so　that　it　can　now　support　the　state-of-art　information　retrieval　methods,　such　as　vector　space,　probabilistic,　and　language　model.　To　evaluate　the　effect　of　the　system　proposed　above,　we＇ve　conducted　a　wide　range　of　experiments　on　benchmark　data　sets,　of　which　the　results　shows　that　solution　can　fulfill　its　purposes　quite　well　in　various　settings.