With　the　emergence　of　various　vehicular　apps　for　automotive　navigation,　driving　safety,　and　in-car　entertainment　system,　vehicular　social　data　is　starting　to　explode.　Such　an　explosion　has　posed　a　great　challenge　for　a　centralized　pattern　of　computing,　storing,　and　managing　information,　calling　for　a　more　decentralized　(cloud-based)　style　of　processing　data　for　vehicular　social　network.　Decentralized　information　processing　requires　encryption　of　sensitive　information　prior　to　out-sourcing　for　the　purpose　of　protecting　data　from　unsolicited　access　and　thus　a　searchable　keyword　index　needs　to　be　extracted　from　the　encrypted　document　for　solicited　access.　However,　most　existing　searchable　encryption　schemes,　based　on　the　words　in　the　subject　or　content,　suffers　from　poor　retrieval　performance.　In　this　study,　a　novel　keyword　extraction　metric　based　on　spatial　distribution　of　a　particular　text　is　proposed　with　the　view　to　improving　the　retrieval　performance.　In　the　proposed　solution,　constrained　information　retrieval　in　vehicle　social　network　is　conceptualized　as　retrieval　of　data　with　extremely　strict　security　and　privacy　preserving　policies　and　a　cloud-based　vehicle　social　information　retrieval　framework　is　developed.　In　line　with　the　framework,　experiments　have　been　conducted　on　benchmark　data　sets　and　diverse　evaluation　metrics.　The　results　suggest　that　the　solution　proposed　in　this　paper　is　capable　of　a　wider　application　and　a　better　performance.