The　great　development　of　smart　networks　enables　Internet　of　Vehicles　(IoV)　as　a　promising　paradigm　to　provide　pervasive　services,　where　privacy　issues　for　location-based　services　(LBSs)　have　attracted　considerable　attention.　In　terms　of　location　privacy,　inspired　by　differential　privacy,　geo-indistinguishability　(Geo-Ind)　has　recently　become　a　prevalent　privacy　model　for　LBSs.　Although　Geo-Ind　guarantees　the　location　privacy,　users＇　other　privacy　concerns　are　still　at　risk　if　the　location　perturbation　behavior　is　exposed　due　to　implausible　reported　locations.　Through　experiments　we　find　the　probability　that　the　classical　Geo-Ind　mechanism　perturbs　the　true　location　to　implausible　areas　can　be　more　than　50%.　To　address　it,　we　first　propose　an　enhanced　privacy　definition　beyond　Geo-Ind,　called　Perturbation-Hidden,　to　prevent　location　perturbation　behaviors　of　users　from　being　recognized　by　guaranteeing　their　pseudo-locations　plausible.　Then　we　design　a　mechanism　to　achieve　this　definition　by　transplanting　the　differential　private　exponential　mechanism　to　our　approach.　Furthermore,　we　propose　a　method　for　determining　the　retrieval　area　utilizing　dynamic　programming　to　ensure　the　accuracy　of　LBSs.　Finally,　we　theoretically　prove　that　our　mechanism　satisfies　the　privacy　definition.　Extensive　experiments　on　simulations　and　a　real-world　dataset　show　that　our　proposal　achieves　100%　plausible　pseudo-locations　while　ensuring　high　query　precision　and　recall.