Cloud-assisted　e-healthcare　sharing　systems　(EHSSs)　play　an　increasingly　pivotal　role　in　the　contemporary　healthcare　field.　By　outsourcing　electronic　medical　records　(EMRs)　to　the　cloud,　hospitals　can　alleviate　local　storage　and　management　burdens　while　facilitating　data　sharing.　Due　to　the　highly　sensitive　nature　of　EMRs,　encryption　is　necessary　before　storing　them　on　the　cloud.　Attribute-based　keyword　search　(ABKS)　enables　the　privacy　protection　of　EMRs　with　efficient　search　services.　However,　there　remain　some　limitations　in　practical　application.　Firstly,　most　ABKS　schemes　only　support　single　keyword　queries,　resulting　in　inaccurate　results　and　wastage　of　computing　and　bandwidth　resources.　Secondly,　since　sensitive　information　within　EMRs　is　encrypted　as　a　whole,　different　data　users　(including　internal　doctors　and　external　researchers)　should　have　varying　access　rights　to　prevent　leakage　of　this　sensitive　information.　Thirdly,　incorrect　search　results　could　lead　to　misdiagnosis　or　endanger　patients＇　lives　and　affect　researchers＇　decision-making　processes.　To　effectively　tackle　these　challenges,　this　paper　proposes　a　verifiable　attribute-based　multi-keyword　search　scheme　with　sensitive　information　hiding　(VABMKS-SIH)　for　cloud-assisted　EHSSs,　where　we　present　a　secure　model　for　multi-　keyword　search　with　two-level　access　structure　by　incorporating　an　improved　blindness　filtering　technique　into　ciphertext-policy　attribute-based　encryption　(CP-ABE)　within　existing　keyword　search　framework.　Our　scheme　employs　a　super-increasing　sequence　to　aggregate　multiple　filtered　data　blocks　into　one　unified　ciphertext,　thereby　greatly　reducing　communication　overhead　during　the　transmission　phases　of　ciphertext.　To　check　the　correctness　of　returned　results,　we　introduce　a　lightweight　algebraic　signature　algorithm　based　on　fundamental　algebraic　operations.　A　security　analysis　demonstrates　that　VABMKS-SIH　is　provably　secure　under　the　random　oracle　mode.　Additionally,　we　also　evaluate　the　proposed　scheme＇s　performance　to　demonstrate　its　utility　in　cloud-assisted　EHSSs.